D
Deleted member 118
Guest
Got a nice Guinness stew and dumplings cooking for later, that should cheer me upI guess someone got up on the wrong foot. Be happy it’s Friday. New financial year, weekend tomorrow!
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
AusEire
Founding Member. It's ok to say No to Dot Joining
Was saying this exact thing to one of the guys last night. This is shorts playing with themselves for sure. The trading volume is incredibly low.
Heavily manipulated
Fullmoonfever
Top 20
I have been wondering too if that is an option with the M4 for those clients wanting an AI accelerator or add on white label IP (Akida) for their "own" NPU.
Here is another case I'm trying to see how is set up but only just started digging.
These recent boards and latest NPUs from Kneron just come out the past month or so.
Maybe @Diogenese could cast an eye if it hasn't already been looked at or discussed yet?
Was something the in the wording of their new board which I've highlighted.
The NPU designed by ARM architecture...huh. Makes sense to say that about the M4 but the NPU?
Couple links below and the site has datasheet etc but doesn't explain much around the NPU and runs 2 X M4.
Did notice though the reference to LTSM in support models so maybe not related at all?
Mini-AI-720
AI Edge Computing Module with Kneron KL720 NPU
Overview
AI Edge Computing Module with Kneron KL720 NPUFeatures
- Kneron KL720 NPU (Designed by ARM architecture)
- Mini card (PCIe[x1] interface,Full size)
- Accelerator for AI Edge Computing
- Enhanced performance to process high resolution video and graphic related computing
AI Edge Computing Module with Kneron KL720 NPU
AI Edge Computing Module with Kneron KL720 NPU
www.aaeon.com
Kneron edge AI chips: local AI, private and secure
Kneron's edge AI SoCs enable low power AI processing on any device with a sensor. Since none of the data collected goes to the cloud to be inferred, data is private and secure. Contact us to learn more.
Last edited:
Boab
I wish I could paint like Vincent
A little bit more info about the upcoming interview/podcast
Laguna Hills, Calif. – June 30, 2022 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of neuromorphic AI IP, today announced that Chief Marketing Officer, Jerome Nadel is the featured guest on the latest “This is our Mission” podcast. Nadel joins Vice President of Worldwide Sales, Rob Telson to discuss the opportunities for BrainChip’s neuromorphic technology to become the de facto standard in AI at the Edge. The podcast will be available Wednesday, July 6, 2022, at 3:00 PDT on BrainChip’s website and across popular podcast platforms.
Nadel is well known for his in-depth and keen understanding of technology with experience in the semiconductor, software, and security industries. With a focus on end-user perspective to technology, and how technology and storytelling go hand in hand, Nadel shares his thoughts about how customers will interface and engage with technology, how sensors have made the digital world easier to use, and how a light, minimalistic approach to learning at the Edge will provide a dramatic impact on society.
“Since joining BrainChip 6 months ago, among other achievements, Jerome has led the company’s rebrand, and reinforced our positioning around Essential AI,” said Telson. “This podcast provides a first-hand, deep dive into Jerome’s mindset about the role of sensors and AI in digital user experience, and how the near future will see end-user improvements inconceivable only years ago.”
The “This is Our Mission” podcast provides AI industry insight to listeners including users, developers, analysts, technical and financial press, and investors. Past episodes are available here.
About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)
BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market neuromorphic processor, AkidaTM, mimics the human brain to analyze only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Keeping machine learning local to the chip, independent of the cloud, also dramatically reduces latency while improving privacy and data security. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers’ products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.
Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006
###
Media Contact:
Mark Smith
JPR Communications
818-398-1424
Investor Contact:
Mark Komonoski
Laguna Hills, Calif. – June 30, 2022 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of neuromorphic AI IP, today announced that Chief Marketing Officer, Jerome Nadel is the featured guest on the latest “This is our Mission” podcast. Nadel joins Vice President of Worldwide Sales, Rob Telson to discuss the opportunities for BrainChip’s neuromorphic technology to become the de facto standard in AI at the Edge. The podcast will be available Wednesday, July 6, 2022, at 3:00 PDT on BrainChip’s website and across popular podcast platforms.
Nadel is well known for his in-depth and keen understanding of technology with experience in the semiconductor, software, and security industries. With a focus on end-user perspective to technology, and how technology and storytelling go hand in hand, Nadel shares his thoughts about how customers will interface and engage with technology, how sensors have made the digital world easier to use, and how a light, minimalistic approach to learning at the Edge will provide a dramatic impact on society.
“Since joining BrainChip 6 months ago, among other achievements, Jerome has led the company’s rebrand, and reinforced our positioning around Essential AI,” said Telson. “This podcast provides a first-hand, deep dive into Jerome’s mindset about the role of sensors and AI in digital user experience, and how the near future will see end-user improvements inconceivable only years ago.”
The “This is Our Mission” podcast provides AI industry insight to listeners including users, developers, analysts, technical and financial press, and investors. Past episodes are available here.
About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)
BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market neuromorphic processor, AkidaTM, mimics the human brain to analyze only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Keeping machine learning local to the chip, independent of the cloud, also dramatically reduces latency while improving privacy and data security. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers’ products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.
Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006
###
Media Contact:
Mark Smith
JPR Communications
818-398-1424
Investor Contact:
Mark Komonoski
After the most recent AGM, I would have expected to see some sizable increase in the upcoming BRN company 4C revenue report especially given the fact that Sean H was so adamant at that meeting that we were to start now watching the quarterly results for anticipated financial progress.Goodbye to that Financial year...I'd suggest a lot of us made some profit, but I personally am not expecting to see any
revenue of any substance in the upcoming 4c being delivered later next month..if products had been released in the
previous 3 months, well, we would or someone would have that information and divulged it by now.
The only revenue would be from IP Licences...correct me if I've forgotten something, which does happen quite a bit these days, as in,
what did I come into this room for ?!!!
Depending on all this shorting behaviour and the "washing" that's been taking place this month (in my opinion) the share price could
well take another hit in late July, as the recurring pattern of no revenue, seems to send a message to shorters, downrampers to try to
savage our stock once again, and (in my opinion) this pattern will only change once "explosive revenue" shows it's face, and is repeated
quarter on quarter....which I'm expecting like many of you.
My love affair with Brainchip is as strong as ever, too emotional for some ?....that thought just makes me laugh
From a perfect clear evening in Perth (but cool).....Tech x
Yes correct @Stable Genius i am also wants to listen to this guy specially after joining BRN! I listened to all his speeches and talks before joining our company and he was great!
I think his marketing strategy and philosophy on value and product preposition based on collaboration between market technology participants and key players because he strongly believes that in today’s market you will never win the market share alone, you need to partner with other market players so everyone is winning & sharing and your business model is going to more durable and sustainable in the long term. Which is great strategy IMO and this is what BRN is doing and executing right now with forming all these partnerships so far.
Cheers
Well, back in 2016, Kneron had a synchronous NN:I have been wondering too if that is an option with the M4 for those clients wanting an AI accelerator or add on white label IP (Akida) for their "own" NPU.
Here is another case I'm trying to see how is set up but only just started digging.
These recent boards and latest NPUs from Kneron just come out the past month or so.
Maybe @Diogenese could cast an eye if it hasn't already been looked at or discussed yet?
Was something the in the wording of their new board which I've highlighted.
The NPU designed by ARM architecture...huh. Makes sense to say that about the M4 but the NPU?
Couple links below and the site has datasheet etc but doesn't explain much around the NPU and runs 2 X M4.
Did notice though the reference to LTSM in support models so maybe not related at all?
Mini-AI-720
AI Edge Computing Module with Kneron KL720 NPU
Overview
AI Edge Computing Module with Kneron KL720 NPU
Features
- Kneron KL720 NPU (Designed by ARM architecture)
- Mini card (PCIe[x1] interface,Full size)
- Accelerator for AI Edge Computing
- Enhanced performance to process high resolution video and graphic related computing
View attachment 10549
AI Edge Computing Module with Kneron KL720 NPU
AI Edge Computing Module with Kneron KL720 NPU
Kneron edge AI chips: local AI, private and secure
Kneron's edge AI SoCs enable low power AI processing on any device with a sensor. Since none of the data collected goes to the cloud to be inferred, data is private and secure. Contact us to learn more.www.kneron.com
US2017330069A1 MULTI-LAYER ARTIFICIAL NEURAL NETWORK AND CONTROLLING METHOD THEREOF
A multi-layer artificial neural network including a plurality of artificial neurons, a storage device, and a controller is provided. The plurality of artificial neurons are used for performing computation based on plural parameters. The storage device is used for storing plural sets of parameters, each set of parameters being corresponding to a respective layer. At a first time instant, the controller controls the storage device to provide a set of parameters corresponding to a first layer to the plurality of artificial neurons so that the plurality of artificial neurons form at least part of the first layer. At a second time instant, the controller controls the storage device to provide a set of parameters corresponding to a second layer to the plurality of artificial neurons so that the plurality of artificial neurons format least part of the second layer.
I see they also dabbled in MemRistors:
US10839893B2 Memory cell with charge trap transistors and method thereof capable of storing data by trapping or detrapping charges
A memory cell includes a first charge trap transistor and a second charge trap transistor. The first charge trap transistor has a substrate, a first terminal coupled to a first bitline, a second terminal coupled to a signal line, a control terminal coupled to a wordline, and a dielectric layer formed between the substrate of the first charge trap transistor and the control terminal of the first charge trap transistor. The second charge trap transistor has a substrate, a first terminal coupled to the signal line, a second terminal coupled to a second bitline, a control terminal coupled to the wordline, and a dielectric layer between the substrate of the second charge trap transistor and the control terminal of the second charge trap transistor. Charges are either trapped to or detrapped from the dielectric layer of the first charge trap transistor when writing data to the memory cell.
More recently, they have been dabbling in back propagation training for NNs, but that document is in Chinese:
CN113240075A MSVL-based BP neural network construction and training method, and MSVL-based BP neural network construction and training system
But do they have anything we need to worry about?
https://www.kneron.com/en/news/blog/106/
Kneron Unveils Next-Gen AI Chip — No Compromise AI For Smart Devices
Kneron’s KL720 chip provides best-in-class performance, energy-efficiency, privacy, and security for consumer smart devicesSan Diego, CA, August 27th, 2020
KL720 is not only the most powerful and energy-efficient chip Kneron has built, it also outclasses competing offerings. Compared to Intel’s Movidius AI chips, KL720 is twice as energy-efficient for similar performance and at half the cost. A DJI drone that currently uses a Movidius chip would double its battery life by using a Kneron chip, without any loss of power. Kneron’s solution can be used in devices that would not be practical for Intel’s chips, either because they’re too expensive or they require too much battery power to operate. KL720 is also 4x more efficient than Google’s Coral edge TPU according to MobileNetV2 benchmark results.
alwaysgreen
Top 20
I believe they sell 40 or 50 million-ish a year (they never release exact sales numbers). Akida inside would definitely make me switch from Google to Apple.
Fullmoonfever
Top 20
Thanks D.
So nothing to see here really unless their previous forays have been superceded with our IP?
Fullmoonfever
Top 20
Recent article on Risc-V.
Interesting the author seems to believe NVIDIA still making a play for ARM.
Snipped one bit re SiFive. Nice to see they work with Microchip. Be good to get embedded with them as well.
www.fosslife.org
What is RISC-V?
BY ERIC STEPHEN BROWN, 08 JUNE, 2021
SiFive — Silicon Valley based SiFive, which is staffed by some of the founders of RISC-V, has led early development in core IP and silicon, both in microcontrollers and higher-end Linux chips. Last fall, SiFive announced a 64-bit, Cortex-A55 like FU740 SoC and launched its second Linux-driven dev kit based on the penta-core FU740 called the HiFive Unmatched. SiFive recently announced a SiFive Intelligence X280 core with AI capabilities enabled via RISC-V’s new RV64X vector extensions. We can soon expect to see a Cortex-A72-like U8-Series.
SiFive cores are also being used by other chipmakers. Microchip, which has been a major RISC-V player in MCUs, combined SiFive’s earlier FU540 cores with its own PolarFire FPGA in its Linux-ready PolarFire SoC, which is available on a PolarFire SoC Icicle Kit.
Interesting the author seems to believe NVIDIA still making a play for ARM.
Snipped one bit re SiFive. Nice to see they work with Microchip. Be good to get embedded with them as well.
What is RISC-V?
Everything you need to know about RISC-V from our expert explainer Eric Brown.
What is RISC-V?
BY ERIC STEPHEN BROWN, 08 JUNE, 2021
SiFive — Silicon Valley based SiFive, which is staffed by some of the founders of RISC-V, has led early development in core IP and silicon, both in microcontrollers and higher-end Linux chips. Last fall, SiFive announced a 64-bit, Cortex-A55 like FU740 SoC and launched its second Linux-driven dev kit based on the penta-core FU740 called the HiFive Unmatched. SiFive recently announced a SiFive Intelligence X280 core with AI capabilities enabled via RISC-V’s new RV64X vector extensions. We can soon expect to see a Cortex-A72-like U8-Series.
SiFive cores are also being used by other chipmakers. Microchip, which has been a major RISC-V player in MCUs, combined SiFive’s earlier FU540 cores with its own PolarFire FPGA in its Linux-ready PolarFire SoC, which is available on a PolarFire SoC Icicle Kit.
Fact Finder
Top 20
I have it on good authority from someone who lives in a barrel that for the following patent to be actioned they would need to already have access to a convolutional spiking neural network processor.WHITE PAPER
Neuromorphic Computing Brings AI to the Edge
How conventional processor architecture is becoming a thing of the past
Connected devices driven by 5G and the Internet of Things (IoT) are everywhere from autonomous vehicles, smart homes, healthcare to space exploration. Devices are becoming more intelligent. Massive amounts of data from multiple sources need to be processed quickly, securely, and in real time, having low latency. Cloud-based architecture may not fulfil these needs of futuristic AI-based systems, that require intelligence at the edge and the ability to process sparse events. Neuromorphic computing resolves the issues of the conventional processor architecture or the von Neumann architecture by separating processing and memory units. It mimics the human brain and its cognitive functions such as interpretation, autonomous adaptation; as well as supports in-memory processing at higher speeds, complexity, and better energy efficiency. As research continues, neuromorphic processors will advance edge computing capabilities and bring AI closer to the edge.
Click on the "Read More" button to read the entire whitepaper.
Click on the contact icon at the bottom right to talk to our subject matter experts.
Read More
Arijit Mukherjee
Senior Scientist, TCS Research
Sounak Dey
Senior Scientist, TCS Research
Vedvyas Krishnamoorthy
Business Development Manager, Technology Business Unit, TCS
Now some might completely discount the fact that Arijit Mukherjee from the above article who is one of the inventors of the following patent and who was a member of the Brainchip Tata team that presented a joint demonstration on 14.12.19 of AKIDA technology performing live gesture recognition and that Brainchip having the only commercially available patent protected convolutional spiking neural network chip in the world 3 years ahead of anyone else as proving or even pointing to Brainchip as providing this chip to Tata but I am not in that camp.
This is one huge statement for TATA to make in my opinion: "Neuromorphic Computing Brings AI to the Edge How conventional processor architecture is becoming a thing of the past".
My opinion only DYOR
FF
AKIDA BALLISTA
US Patent for System and method of gesture recognition using a reservoir based convolutional spiking neural network Patent (Patent # 11,256,954 issued February 22, 2022) - Justia Patents Search
This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least...
patents.justia.com
System and method of gesture recognition using a reservoir based convolutional spiking neural network
Dec 17, 2020This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least one gestures from a plurality of gestures is preprocessed to obtain plurality of spike frames. The plurality of spike frames is processed by a multi layered convolutional spiking neural network to learn plurality of spatial features from the at least one gesture. A filter block is deactivated from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt. A spatio-temporal features is obtained by allowing the spike activations from CSNN layer to flow through the reservoir. The spatial feature is classified by classifier from the CSNN layer and the spatio-temporal features from the reservoir to obtain set of prioritized gestures.
Skip to: Description · Claims · References Cited · Patent History · Patent History
Description
PRIORITY CLAIM
This U.S. patent application claims priority under 35 U.S.C. § 119 to: India Application No. 202021025784, filed on Jun. 18, 2020. The entire contents of the aforementioned application are incorporated herein by reference.
TECHNICAL FIELD
This disclosure relates generally to gesture recognition, and, more particularly, to system and method of gesture recognition using a reservoir based convolutional spiking neural network.
BACKGROUND
In an age of artificial intelligence, robots and drones are key enablers of task automation and they are being used in various domains such as manufacturing, healthcare, warehouses, disaster management etc. As a consequence, they often need to share work-space with and interact with human workers and thus evolving the area of research named Human Robot Interaction (HRI). Problems in this domain are mainly centered around learning and identifying of gestures/speech/intention of human coworkers along with classical problems of learning and identification of surrounding environment (and obstacles, objects etc. therein). All these essentially are needed to be done in a dynamic and noisy practical work environment. As of current state of the art vision based solutions using artificial neural networks (including deep neural networks) have high accuracy, however the models are not the most efficient solutions as learning methods and inference frameworks of the conventional deep neural networks require huge amount of training data and are typically compute and energy intensive. They are also bounded by one or more conventional architectures that leads to data transfer bottleneck between memory and processing units and related power consumption issues. Hence, this genre of solutions does not really help robots and drones to do their jobs as they are classically constrained by their battery life.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor implemented method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network is provided. The processor implemented method includes at least one of: receiving, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocessing, via one or more hardware processors, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivating, via the one or more hardware processors, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtaining, via the one or more hardware processors, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams are represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
In another aspect, there is provided a system to identify a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. The system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces. The one or more hardware processors are configured by the instructions to: receive, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocess, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; process, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivate, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtain, spatiotemporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classify, by a classifier, the at least one of spatial feature from the CSNN layer and the spatiotemporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams is represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes at least one of: receiving, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocessing, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivating, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtaining, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams are represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed....
Claims
1. A processor implemented method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network, comprising:receiving, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record;preprocessing, via one or more hardware processors, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames;processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers;deactivating, via the one or more hardware processors, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism;obtaining, via the one or more hardware processors, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; andclassifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures.
2. The processor implemented method of claim 1, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
3. The processor implemented method of claim 1, wherein a plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
4. The processor implemented method of claim 1, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
5. The processor implemented method of claim 1, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
6. The processor implemented method of claim 1, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
7. A system (100) to identify a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network, comprising:
a memory (102) storing instructions;one or more communication interfaces (106); andone or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to: receive, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record; preprocess, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; process, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers; deactivate, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism; obtain, spatiotemporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; and classify, by a classifier, the at least one of spatial feature from the CSNN layer and the spatiotemporal features from the reservoir to obtain a set of prioritized gestures.
8. The system (100) of claim 7, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
9. The system (100) of claim 7, wherein plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
10. The system (100) of claim 7, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
11. The system (100) of claim 7, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
12. The system (100) of claim 7, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
13. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions comprising:
receiving, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record;preprocessing, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames;processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers;deactivating, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism;obtaining, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; andclassifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures.
14. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
15. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
16. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
17. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
18. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
Referenced Cited
U.S. Patent Documents
| 6028626 | February 22, 2000 | Aviv |
| 6236736 | May 22, 2001 | Crabtree |
| 6701016 | March 2, 2004 | Jojic |
| 7152051 | December 19, 2006 | Commons |
| 7280697 | October 9, 2007 | Perona |
| 8504361 | August 6, 2013 | Collobert |
| 8811726 | August 19, 2014 | Belhumeur |
| 8942466 | January 27, 2015 | Petre et al. |
| 9015093 | April 21, 2015 | Commons |
| 9299022 | March 29, 2016 | Buibas et al. |
| 109144260 | January 2019 | CN |
| WO2019074532 | April 2019 | WO |
- Panda, Priyadarshini et al., “Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model,” Frontiers in Neuroscience, Oct. 2017, Publisher: Arxiv Link: https://arxiv.org/pdf/1710.07354.pdf.
Patent number: 11256954
Type: Grant
Filed: Dec 17, 2020
Date of Patent: Feb 22, 2022
Patent Publication Number: 20210397878
Assignee: Tala Consultancy Services Limited (Mumbai)
Inventors: Arun George (Bangalore), Dighanchal Banerjee (Kolkata), Sounak Dey (Kolkata), Arijit Mukherjee (Kolkata)
Primary Examiner: Yosef Kassa
Application Number: 17/124,584
Classifications
Current U.S. Class: Intrusion Detection (348/152)
International Classification: G06K 9/62 (20060101); G06K 9/00 (20060101); G06N 3/04 (20060101);
Is that suit dumplings Rocket, ? Just love em, not what one would call gourmet cuisine these days, but oh, they are so good, cheer you up, indeed, they will. Got an appetite now, wonder if we have some suit. ?
hotty...
Fact Finder
Top 20
There are interesting things happening with Tata. The first is they appear to be in line to produce components and electronics for the India Apple IPhone. The second is what their company Titan is planning to bring to market in India in the wearables area:I have it on good authority from someone who lives in a barrel that for the following patent to be actioned they would need to already have access to a convolutional spiking neural network processor.
Now some might completely discount the fact that Arijit Mukherjee from the above article who is one of the inventors of the following patent and who was a member of the Brainchip Tata team that presented a joint demonstration on 14.12.19 of AKIDA technology performing live gesture recognition and that Brainchip having the only commercially available patent protected convolutional spiking neural network chip in the world 3 years ahead of anyone else as proving or even pointing to Brainchip as providing this chip to Tata but I am not in that camp.
This is one huge statement for TATA to make in my opinion: "Neuromorphic Computing Brings AI to the Edge How conventional processor architecture is becoming a thing of the past".
My opinion only DYOR
FF
AKIDA BALLISTA
US Patent for System and method of gesture recognition using a reservoir based convolutional spiking neural network Patent (Patent # 11,256,954 issued February 22, 2022) - Justia Patents Search
This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least...
System and method of gesture recognition using a reservoir based convolutional spiking neural network
Dec 17, 2020
This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least one gestures from a plurality of gestures is preprocessed to obtain plurality of spike frames. The plurality of spike frames is processed by a multi layered convolutional spiking neural network to learn plurality of spatial features from the at least one gesture. A filter block is deactivated from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt. A spatio-temporal features is obtained by allowing the spike activations from CSNN layer to flow through the reservoir. The spatial feature is classified by classifier from the CSNN layer and the spatio-temporal features from the reservoir to obtain set of prioritized gestures.
Skip to: Description · Claims · References Cited · Patent History · Patent History
Description
PRIORITY CLAIM
This U.S. patent application claims priority under 35 U.S.C. § 119 to: India Application No. 202021025784, filed on Jun. 18, 2020. The entire contents of the aforementioned application are incorporated herein by reference.
TECHNICAL FIELD
This disclosure relates generally to gesture recognition, and, more particularly, to system and method of gesture recognition using a reservoir based convolutional spiking neural network.
BACKGROUND
In an age of artificial intelligence, robots and drones are key enablers of task automation and they are being used in various domains such as manufacturing, healthcare, warehouses, disaster management etc. As a consequence, they often need to share work-space with and interact with human workers and thus evolving the area of research named Human Robot Interaction (HRI). Problems in this domain are mainly centered around learning and identifying of gestures/speech/intention of human coworkers along with classical problems of learning and identification of surrounding environment (and obstacles, objects etc. therein). All these essentially are needed to be done in a dynamic and noisy practical work environment. As of current state of the art vision based solutions using artificial neural networks (including deep neural networks) have high accuracy, however the models are not the most efficient solutions as learning methods and inference frameworks of the conventional deep neural networks require huge amount of training data and are typically compute and energy intensive. They are also bounded by one or more conventional architectures that leads to data transfer bottleneck between memory and processing units and related power consumption issues. Hence, this genre of solutions does not really help robots and drones to do their jobs as they are classically constrained by their battery life.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor implemented method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network is provided. The processor implemented method includes at least one of: receiving, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocessing, via one or more hardware processors, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivating, via the one or more hardware processors, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtaining, via the one or more hardware processors, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams are represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
In another aspect, there is provided a system to identify a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. The system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces. The one or more hardware processors are configured by the instructions to: receive, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocess, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; process, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivate, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtain, spatiotemporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classify, by a classifier, the at least one of spatial feature from the CSNN layer and the spatiotemporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams is represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes at least one of: receiving, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocessing, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivating, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtaining, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams are represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed....
Claims
1. A processor implemented method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network, comprising:
receiving, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record;preprocessing, via one or more hardware processors, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames;processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers;deactivating, via the one or more hardware processors, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism;obtaining, via the one or more hardware processors, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; andclassifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures.
2. The processor implemented method of claim 1, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
3. The processor implemented method of claim 1, wherein a plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
4. The processor implemented method of claim 1, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
5. The processor implemented method of claim 1, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
6. The processor implemented method of claim 1, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
7. A system (100) to identify a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network, comprising:
a memory (102) storing instructions;one or more communication interfaces (106); andone or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to: receive, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record; preprocess, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; process, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers; deactivate, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism; obtain, spatiotemporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; and classify, by a classifier, the at least one of spatial feature from the CSNN layer and the spatiotemporal features from the reservoir to obtain a set of prioritized gestures.
8. The system (100) of claim 7, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
9. The system (100) of claim 7, wherein plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
10. The system (100) of claim 7, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
11. The system (100) of claim 7, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
12. The system (100) of claim 7, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
13. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions comprising:
receiving, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record;preprocessing, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames;processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers;deactivating, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism;obtaining, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; andclassifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures.
14. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
15. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
16. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
17. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
18. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
Referenced Cited
U.S. Patent Documents
Foreign Patent Documents
6028626 February 22, 2000 Aviv 6236736 May 22, 2001 Crabtree 6701016 March 2, 2004 Jojic 7152051 December 19, 2006 Commons 7280697 October 9, 2007 Perona 8504361 August 6, 2013 Collobert 8811726 August 19, 2014 Belhumeur 8942466 January 27, 2015 Petre et al. 9015093 April 21, 2015 Commons 9299022 March 29, 2016 Buibas et al.
Other references
109144260 January 2019 CN WO2019074532 April 2019 WO
Patent History
- Panda, Priyadarshini et al., “Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model,” Frontiers in Neuroscience, Oct. 2017, Publisher: Arxiv Link: https://arxiv.org/pdf/1710.07354.pdf.
Patent number: 11256954
Type: Grant
Filed: Dec 17, 2020
Date of Patent: Feb 22, 2022
Patent Publication Number: 20210397878
Assignee: Tala Consultancy Services Limited (Mumbai)
Inventors: Arun George (Bangalore), Dighanchal Banerjee (Kolkata), Sounak Dey (Kolkata), Arijit Mukherjee (Kolkata)
Primary Examiner: Yosef Kassa
Application Number: 17/124,584
Classifications
Current U.S. Class: Intrusion Detection (348/152)
International Classification: G06K 9/62 (20060101); G06K 9/00 (20060101); G06N 3/04 (20060101);
Wearing The Future | Tata group
Business,Consumer and Retail,Innovation,Retail & Ecommerce,Technology
My opinion only DYOR
FF
AKIDA BALLISTA
Afternoon Chippers,
Great find on the above Fact Finder.
Hard to believe for the price of a Iceberg Lettuce, $9.90 AU, (which is 96% water) one if so inclined could purchase 11.927 shares in Brainchip, the most revolutionary tech company going, on the global stage.
Rob Telson was correct when he said tip of the Iceberg, 1 BRN share pressently would only buy 0ne twelfth of a lettuce..... just the tip.
Some times one just has to laugh at the stupidity in the world.
Top job all with all the sleuthing , it will happen.... eventually.
Regards,
Esq.
Great find on the above Fact Finder.
Hard to believe for the price of a Iceberg Lettuce, $9.90 AU, (which is 96% water) one if so inclined could purchase 11.927 shares in Brainchip, the most revolutionary tech company going, on the global stage.
Rob Telson was correct when he said tip of the Iceberg, 1 BRN share pressently would only buy 0ne twelfth of a lettuce..... just the tip.
Some times one just has to laugh at the stupidity in the world.
Top job all with all the sleuthing , it will happen.... eventually.
Regards,
Esq.
Makeme 2020
Regular
Short video from Renesas.
Learning
Learning to the Top 🕵♂️
Good morning all from VN.
Its nice to see some Green grass below the horizon. Just as the SP.
Maybe I should leave Aus more often, I left yesterday at market closed. Wake up in another country and the SP is green.
Be patient my fellow shareholder, Sunshine and Green fields is always after the rain.
Learning
It's great to be a shareholder.
Its nice to see some Green grass below the horizon. Just as the SP.
Maybe I should leave Aus more often, I left yesterday at market closed. Wake up in another country and the SP is green.
Be patient my fellow shareholder, Sunshine and Green fields is always after the rain.
Learning
It's great to be a shareholder.
D
Deleted member 118
Guest
Just made a chocolate and wallnut brownie cake for deserts later as well, just needs popping in the oven and served with either custard or ice cream
And one man size pot of stew that has been slow cooking for 5 hours and about to add the potatoes already for my poker playing friends later.
Last edited by a moderator:
Fullmoonfever
Top 20
I have it on good authority from someone who lives in a barrel that for the following patent to be actioned they would need to already have access to a convolutional spiking neural network processor.
Now some might completely discount the fact that Arijit Mukherjee from the above article who is one of the inventors of the following patent and who was a member of the Brainchip Tata team that presented a joint demonstration on 14.12.19 of AKIDA technology performing live gesture recognition and that Brainchip having the only commercially available patent protected convolutional spiking neural network chip in the world 3 years ahead of anyone else as proving or even pointing to Brainchip as providing this chip to Tata but I am not in that camp.
This is one huge statement for TATA to make in my opinion: "Neuromorphic Computing Brings AI to the Edge How conventional processor architecture is becoming a thing of the past".
My opinion only DYOR
FF
AKIDA BALLISTA
US Patent for System and method of gesture recognition using a reservoir based convolutional spiking neural network Patent (Patent # 11,256,954 issued February 22, 2022) - Justia Patents Search
This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least...
System and method of gesture recognition using a reservoir based convolutional spiking neural network
Dec 17, 2020
This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least one gestures from a plurality of gestures is preprocessed to obtain plurality of spike frames. The plurality of spike frames is processed by a multi layered convolutional spiking neural network to learn plurality of spatial features from the at least one gesture. A filter block is deactivated from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt. A spatio-temporal features is obtained by allowing the spike activations from CSNN layer to flow through the reservoir. The spatial feature is classified by classifier from the CSNN layer and the spatio-temporal features from the reservoir to obtain set of prioritized gestures.
Skip to: Description · Claims · References Cited · Patent History · Patent History
Description
PRIORITY CLAIM
This U.S. patent application claims priority under 35 U.S.C. § 119 to: India Application No. 202021025784, filed on Jun. 18, 2020. The entire contents of the aforementioned application are incorporated herein by reference.
TECHNICAL FIELD
This disclosure relates generally to gesture recognition, and, more particularly, to system and method of gesture recognition using a reservoir based convolutional spiking neural network.
BACKGROUND
In an age of artificial intelligence, robots and drones are key enablers of task automation and they are being used in various domains such as manufacturing, healthcare, warehouses, disaster management etc. As a consequence, they often need to share work-space with and interact with human workers and thus evolving the area of research named Human Robot Interaction (HRI). Problems in this domain are mainly centered around learning and identifying of gestures/speech/intention of human coworkers along with classical problems of learning and identification of surrounding environment (and obstacles, objects etc. therein). All these essentially are needed to be done in a dynamic and noisy practical work environment. As of current state of the art vision based solutions using artificial neural networks (including deep neural networks) have high accuracy, however the models are not the most efficient solutions as learning methods and inference frameworks of the conventional deep neural networks require huge amount of training data and are typically compute and energy intensive. They are also bounded by one or more conventional architectures that leads to data transfer bottleneck between memory and processing units and related power consumption issues. Hence, this genre of solutions does not really help robots and drones to do their jobs as they are classically constrained by their battery life.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor implemented method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network is provided. The processor implemented method includes at least one of: receiving, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocessing, via one or more hardware processors, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivating, via the one or more hardware processors, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtaining, via the one or more hardware processors, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams are represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
In another aspect, there is provided a system to identify a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. The system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces. The one or more hardware processors are configured by the instructions to: receive, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocess, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; process, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivate, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtain, spatiotemporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classify, by a classifier, the at least one of spatial feature from the CSNN layer and the spatiotemporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams is represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes at least one of: receiving, from a neuromorphic event camera, two-dimensional spike streams as an input; preprocessing, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture; deactivating, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt; obtaining, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir; and classifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures. In an embodiment, the two-dimensional spike streams are represented as an address event representation (AER) record. In an embodiment, each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers. In an embodiment, the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism. In an embodiment, the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof.
In an embodiment, the spike streams may be compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity. In an embodiment, plurality of learned different spatially co-located features may be distributed on the plurality of filters from the plurality of filter blocks. In an embodiment, a special node between filters of the filter block may be configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters. In an embodiment, a plurality of weights of a synapse between input and the CSNN layer may be learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer. In an embodiment, the reservoir may include a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed....
Claims
1. A processor implemented method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network, comprising:
receiving, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record;preprocessing, via one or more hardware processors, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames;processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers;deactivating, via the one or more hardware processors, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism;obtaining, via the one or more hardware processors, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; andclassifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures.
2. The processor implemented method of claim 1, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
3. The processor implemented method of claim 1, wherein a plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
4. The processor implemented method of claim 1, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
5. The processor implemented method of claim 1, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
6. The processor implemented method of claim 1, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
7. A system (100) to identify a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network, comprising:
a memory (102) storing instructions;one or more communication interfaces (106); andone or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to: receive, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record; preprocess, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames; process, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers; deactivate, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism; obtain, spatiotemporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; and classify, by a classifier, the at least one of spatial feature from the CSNN layer and the spatiotemporal features from the reservoir to obtain a set of prioritized gestures.
8. The system (100) of claim 7, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
9. The system (100) of claim 7, wherein plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
10. The system (100) of claim 7, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
11. The system (100) of claim 7, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
12. The system (100) of claim 7, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
13. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions comprising:
receiving, from a neuromorphic event camera, two-dimensional spike streams as an input, wherein the two-dimensional spike streams are represented as an address event representation (AER) record;preprocessing, the address event representation (AER) record associated with at least one gestures from a plurality of gestures to obtain a plurality of spike frames;processing, by a multi layered convolutional spiking neural network, the plurality of spike frames to learn a plurality of spatial features from the at least one gesture, wherein each sliding convolutional window in the plurality of spike frames are connected to a neuron corresponding to a filter among plurality of filters corresponding to a filter block among plurality of filter blocks in each convolutional layer from plurality of convolutional layers;deactivating, at least one filter block from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt, wherein the plurality of filter blocks are configured to concentrate a plurality of class-wise spatial features to the filter block for learning associated patterns based on a long-term lateral inhibition mechanism;obtaining, spatio-temporal features by allowing the spike activations from a CSNN layer to flow through the reservoir, wherein the CSNN layer is stacked to provide at least one of: (i) a low-level spatial features, (ii) a high-level spatial features, or combination thereof; andclassifying, by a classifier, the at least one of spatial feature from the CSNN layer and the spatio-temporal features from the reservoir to obtain a set of prioritized gestures.
14. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein the spike streams are compressed per neuronal level by accumulating spikes at a sliding window of time, to obtain a plurality of output frames with reduced time granularity.
15. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a plurality of learned different spatially co-located features are distributed on the plurality of filters from the plurality of filter blocks.
16. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a special node between filters of the filter block is configured to switch between different filters based on an associated decay constant to distribute learning of different spatially co-located features on the different filters.
17. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein a plurality of weights of a synapse between input and the CSNN layer are learned using an unsupervised two trace STDP learning rule upon at least one spiking activity of the input layer.
18. The one or more non-transitory machine-readable information storage mediums of claim 13, wherein the reservoir comprises a sparse random cyclic connectivity which acts as a random projection of the input spikes to an expanded spatio-temporal embedding.
Referenced Cited
U.S. Patent Documents
Foreign Patent Documents
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Other references
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Patent History
- Panda, Priyadarshini et al., “Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model,” Frontiers in Neuroscience, Oct. 2017, Publisher: Arxiv Link: https://arxiv.org/pdf/1710.07354.pdf.
Patent number: 11256954
Type: Grant
Filed: Dec 17, 2020
Date of Patent: Feb 22, 2022
Patent Publication Number: 20210397878
Assignee: Tala Consultancy Services Limited (Mumbai)
Inventors: Arun George (Bangalore), Dighanchal Banerjee (Kolkata), Sounak Dey (Kolkata), Arijit Mukherjee (Kolkata)
Primary Examiner: Yosef Kassa
Application Number: 17/124,584
Classifications
Current U.S. Class: Intrusion Detection (348/152)
International Classification: G06K 9/62 (20060101); G06K 9/00 (20060101); G06N 3/04 (20060101);
Haven't looked much into TATA & your post prompted a look.
On Arijit Mukherjee and the below may been posted previously(?) I see he also co-authored a white paper as per snip below.
Is on the TCS website and whilst doesn't appear dated it can be seen that the links on the bottom of pages to some reference material they quote has access dates late Feb 22 which suggests maybe a Mar 22 release earliest?
Creating Futuristic Edge Systems with Neuromorphic Computing
As AI techniques on classical processor architecture become a concern, explore neuromorphic computing to create futuristic systems with intelligence at the edge.
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