BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Sirod69
bavarian girl ;-)
We're turning 100 this year!
Join us along with Chief Executive Officer Christophe Périllat, Group Chief Human Resources Officer Agnès Park and Chief Technology Officer Geoffrey Bouquot as we come together to talk about our past, present and future. We can't wait to see you there!Valeo Turns 100! 🎉 | LinkedIn
We're turning 100 this year! 👴 Join us along with Chief Executive Officer Christophe Périllat, Group Chief Human Resources Officer Agnès Park and Chief Technology Officer Geoffrey Bouquot as we come together to talk about our past, present and future. We can't wait to see you there!
Sirod69
bavarian girl ;-)
Security cameras often record footage that isn’t relevant. To stream all footage to the cloud over wifi is wasteful, consumes Internet bandwidth and shortens battery life. Our on-device People Detection AI enables your cameras to stream and record only when people are in view. Built to be tiny, our AI can even be deployed on small, low-power and low-cost cameras. Give your security cameras the competitive edge by utilizing Plumerai’s People Detection AI.
Find out more at https://lnkd.in/eAGuaPkh
Find out more at https://lnkd.in/eAGuaPkh
Sirod69
bavarian girl ;-)
I know it's nothing new, but it was just posted on Twitter by BRN
BrainChip
@BrainChip_inc
BrainChip, the first commercial producer of ultra-low power, fully digital, neuromorphic AI IP, has achieved tape out of its AKD1500 reference design https://newelectronics.co.uk/content/news/brainchip-achieves-tape-out-of-akd1500-chip…
@New_Electronics
#Neuromorphic #Edge #AI
BrainChip
@BrainChip_inc
BrainChip, the first commercial producer of ultra-low power, fully digital, neuromorphic AI IP, has achieved tape out of its AKD1500 reference design https://newelectronics.co.uk/content/news/brainchip-achieves-tape-out-of-akd1500-chip…
@New_Electronics
#Neuromorphic #Edge #AI
Last edited:
Techinvestor17
Regular
It’s the higher of that or the price Brainchip tell the company
Sirod69
bavarian girl ;-)
I just read that and took a closer look at the people. I'm curious what will come out of the unveiling of this Snapdragon Incredology?
Leonard Lee• 2.Tech Industry & Strategy Advisor dedicated to making you constructively uncomfortable
26 Min.
Hanging out with Alex Katouzian, SVP of Mobile, Compute & XR and Christopher Patrick “Big Island”, SVP of Mobile of Qualcomm at Samsung Electronics’ Galaxy Unpacked 2023 event. Alex promises to that there will be some Snapdragon Incredology unveiled today!
#samsungunpacked #nextcurve #snapdragon #qualcomm
Follow me here and neXt Curve for highlights!
Say hello to the #Snapdragon 8 Gen 2 for Galaxy: the fastest Snapdragon ever, uniquely customized for Samsung Mobile’s new Galaxy S23 series. With accelerated performance and unparalleled connectivity, it unlocks epic experiences across AI, 5G, gaming, camera, and WiFi and Bluetooth thanks to Qualcomm FastConnect. We’re thrilled to be a part of this exciting new series — congratulations, Samsung! #SamsungUnpacked https://bit.ly/3Jpr1ua
www.androidcentral.com
Leonard Lee• 2.Tech Industry & Strategy Advisor dedicated to making you constructively uncomfortable
26 Min.
Hanging out with Alex Katouzian, SVP of Mobile, Compute & XR and Christopher Patrick “Big Island”, SVP of Mobile of Qualcomm at Samsung Electronics’ Galaxy Unpacked 2023 event. Alex promises to that there will be some Snapdragon Incredology unveiled today!
#samsungunpacked #nextcurve #snapdragon #qualcomm
Follow me here and neXt Curve for highlights!
Say hello to the #Snapdragon 8 Gen 2 for Galaxy: the fastest Snapdragon ever, uniquely customized for Samsung Mobile’s new Galaxy S23 series. With accelerated performance and unparalleled connectivity, it unlocks epic experiences across AI, 5G, gaming, camera, and WiFi and Bluetooth thanks to Qualcomm FastConnect. We’re thrilled to be a part of this exciting new series — congratulations, Samsung! #SamsungUnpacked https://bit.ly/3Jpr1ua
Bye-bye Exynos! Galaxy S23 series uses the Snapdragon 8 Gen 2 globally, and it's a win-win for Qualcomm and Samsung
Samsung isn't offering an Exynos variant of the Galaxy S23.
www.androidcentral.com
Last edited:
stuart888
Regular
London Engineers published an informative SNN robotic test with N-of-M coding. The document itself is not that long and very understandable for the non-snn-engineers, regular folks like me still trying to learn.
https://www.mdpi.com/2674-0729/2/1/2/htm
They create a robotic goalie, and use SNN with 2-of-7 for N-of-M Coding. A DVS event-based camera is the primary input sensor device, but also a touchpad on the goalie for reward feedback.
A few of the items that were helpful to me, self-learning.
MicroController Unit (MCU)
The MCU interface board provides a spinal-cord of channel which connects the peripheral organs (vision, sound, actuator sensors) to the brain (SNN), allows them to talk to each other and pre-processes the signals. Graphic below shows the MCU board data flow.
The spinal-cord analogy for the MCU works for me.
N-of-M Coding (definition summary)
In Spiking Neural Networks information is represented as a time-dependent sequence of spikes, such as a sequence of bits transmitted on a channel.
Different coding protocols have been used in SNN each with some limitations. Conventional rate coding counts spikes in fixed time windows to represent each information unit (i.e., encode one alphabet letter or one number), resulting in a relatively slow technique.
A faster coding solution is possible with rank order coding, with whom it is possible to use shorter time windows to represent more bits of information. Therefore, the SNN only gets the N bits, only the actual info needed, not M bits.
For a real-time scenario, the delay must be as short as possible, prioritizing the transmission time and sacrificing the amount of information that can be represented.
In this example, the SNN uses a 2-of-7 coding protocol to communicate with external devices, where the sender just needs to change the logical level of two wires during each symbol transfer. The states of the other five wires are not changed.
SNN with N-of-M Coding = Only good info, no turds!
The document was more informative than the video ironically.
https://www.mdpi.com/2674-0729/2/1/2/htm
They create a robotic goalie, and use SNN with 2-of-7 for N-of-M Coding. A DVS event-based camera is the primary input sensor device, but also a touchpad on the goalie for reward feedback.
A few of the items that were helpful to me, self-learning.
MicroController Unit (MCU)
The MCU interface board provides a spinal-cord of channel which connects the peripheral organs (vision, sound, actuator sensors) to the brain (SNN), allows them to talk to each other and pre-processes the signals. Graphic below shows the MCU board data flow.
The spinal-cord analogy for the MCU works for me.
N-of-M Coding (definition summary)
In Spiking Neural Networks information is represented as a time-dependent sequence of spikes, such as a sequence of bits transmitted on a channel.
Different coding protocols have been used in SNN each with some limitations. Conventional rate coding counts spikes in fixed time windows to represent each information unit (i.e., encode one alphabet letter or one number), resulting in a relatively slow technique.
A faster coding solution is possible with rank order coding, with whom it is possible to use shorter time windows to represent more bits of information. Therefore, the SNN only gets the N bits, only the actual info needed, not M bits.
For a real-time scenario, the delay must be as short as possible, prioritizing the transmission time and sacrificing the amount of information that can be represented.
In this example, the SNN uses a 2-of-7 coding protocol to communicate with external devices, where the sender just needs to change the logical level of two wires during each symbol transfer. The states of the other five wires are not changed.
SNN with N-of-M Coding = Only good info, no turds!
The document was more informative than the video ironically.
Makeme 2020
Regular
Artificial Intelligence
Electronics
CPUs
BrainChip achieves tape out of AKD1500 chip
News1 min read
Neil Tyler
31 Jan 2023
BrainChip, the first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, has achieved tape out of its AKD1500 reference design.
The tape-out was completed using GlobalFoundries’ 22nm fully depleted silicon-on-insulator (FD-SOI) technology and is being described as a milestone in validating BrainChip’s IP across different processes and foundries, providing its partners with varied global manufacturing options.
The AKD1500 uses the latest features of BrainChip’s energy-efficient, event-based, Akida AI IP in combination with the GlobalFoundries’ ultra-low leakage, proprietary FDX process technology platform, providing always-on, at-sensor applications or other low-power embedded applications for the AIoT, industrial, consumer and automotive markets.
In always-on, at-sensor use cases, Akida can completely offload feed-forward networks without the use of a CPU, ensuring energy efficient intelligent sensors. For other low-power embedded applications, the CPU solely performs sensor fusion processes or runs the application with Akida managing all the AI acceleration to significantly minimise CPU workload and system load, freeing up the MCU for other general-purpose computation.
The AKD1500 has been designed as an accelerator reference chip, using quad/octal SPI interfaces for MCU modules and PCIe for extensible cards, which assists partners in developing and demonstrating their solutions as a steppingstone to integrating the Akida IP into their production SoCs.
“This is an important validation milestone in the continuing innovation of our event-based, neuromorphic Akida IP, even if it is fully digital and portable across foundries,” said Anil Mankar, Co-Founder & Chief Development Officer at BrainChip. “The AKD1500 reference chip using GlobalFoundries’ very low-leakage FD SOI platform, showcases the possibilities for intelligent sensors in edge AI.”
Artificial Intelligence
Semiconductors
Electronics
http://www.brainchip.com
alwaysgreen
Top 20
Hmmm. I am doubtful but hopeful.I just read that and took a closer look at the people. I'm curious what will come out of the unveiling of this Snapdragon Incredology?
Leonard Lee• 2.Tech Industry & Strategy Advisor dedicated to making you constructively uncomfortable
26 Min.
Hanging out with Alex Katouzian, SVP of Mobile, Compute & XR and Christopher Patrick “Big Island”, SVP of Mobile of Qualcomm at Samsung Electronics’ Galaxy Unpacked 2023 event. Alex promises to that there will be some Snapdragon Incredology unveiled today!
#samsungunpacked #nextcurve #snapdragon #qualcomm
Follow me here and neXt Curve for highlights!
Say hello to the #Snapdragon 8 Gen 2 for Galaxy: the fastest Snapdragon ever, uniquely customized for Samsung Mobile’s new Galaxy S23 series. With accelerated performance and unparalleled connectivity, it unlocks epic experiences across AI, 5G, gaming, camera, and WiFi and Bluetooth thanks to Qualcomm FastConnect. We’re thrilled to be a part of this exciting new series — congratulations, Samsung! #SamsungUnpacked https://bit.ly/3Jpr1ua
Bye-bye Exynos! Galaxy S23 series uses the Snapdragon 8 Gen 2 globally, and it's a win-win for Qualcomm and Samsung
Samsung isn't offering an Exynos variant of the Galaxy S23.
This image is from the Samsung unpacked event. They mentioned clearer photos. Not many more details unfortunately.
BaconLover
Founding Member
I remember seeing similar last year.
AI camera: What it is, how it works | Samsung Semiconductor Global
Discover how mobile processors with AI (Artificial Intelligence) enable smartphone cameras to capture mesmerizing photography.
stuart888
Regular
Love this "Time-to-first-spike"! I liked the brevity of the robotic goalie.
But to learn N-of-M Coding to the utmost, I know a source!
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548614/
This paper also introduces a third option—“Ranked-NoM” (R-NoM), which combines features from both ROC and NoM coding schemes: only the first N input spikes are propagated, but their order is readout by downstream neurons thanks to inhomogeneous weights and linear desensitization. The unifying mathematical framework allows the three codes to be compared in terms of discriminability, which measures to what extent a neuron responds more strongly to its preferred input spike pattern than to random patterns. This discriminability turns out to be much higher for R-NoM than for the other codes, especially in the early phase of the responses. We also argue that R-NoM is much more hardware-friendly than the original ROC proposal, although NoM remains the easiest to implement in hardware because it only requires binary synapses.
The current analysis provides a strong argument for using implementations that process incoming spikes in order since it is the only way to take advantage of the remarkable early discriminative power of R-NoM coding. Such an approach goes a long way toward ensuring that computations can be done with the minimum number of spiking events.
"Go Spike Go"!
But to learn N-of-M Coding to the utmost, I know a source!
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548614/
This paper also introduces a third option—“Ranked-NoM” (R-NoM), which combines features from both ROC and NoM coding schemes: only the first N input spikes are propagated, but their order is readout by downstream neurons thanks to inhomogeneous weights and linear desensitization. The unifying mathematical framework allows the three codes to be compared in terms of discriminability, which measures to what extent a neuron responds more strongly to its preferred input spike pattern than to random patterns. This discriminability turns out to be much higher for R-NoM than for the other codes, especially in the early phase of the responses. We also argue that R-NoM is much more hardware-friendly than the original ROC proposal, although NoM remains the easiest to implement in hardware because it only requires binary synapses.
The current analysis provides a strong argument for using implementations that process incoming spikes in order since it is the only way to take advantage of the remarkable early discriminative power of R-NoM coding. Such an approach goes a long way toward ensuring that computations can be done with the minimum number of spiking events.
"Go Spike Go"!Thanks Iseki. I've always thought that participants in the EAP would have had a use case for AKIDA in mind when they joined up i.e. they were interested in seeing how AKIDA could improve an already existing product. Perhaps this isn't the case for many of them. The 'delays' in signing IP agreements could suggest that they might be modifying their product offerings, or inventing new ones, to take advantage of what AKIDA can do for them (or waiting for the 1500 chip to test). If this is the case then development delays would be natural. On the other hand I can't recall ever seeing a comprehensive list of EAP participants so BRN may not be required to disclose who joins or who departs the program. Only 4 months to the AGM and no doubt many from the 1000 eyes will attend so hopefully we will have a clearer picture after that. Cheers
wilzy123
Founding Member
Umm.. the company has made it pretty clear that they do not see nvidia as a competitor
wilzy123
Founding Member
Ignored.
stuart888
Regular
2022 Roadmap on Neuromorphic Computing and Engineering
Any dots in this group of trained neural nuts? Lots of names and neuro-job-titles.
Only 169 pages to read. Let me summarize: "you guessed it", proclaiming Go Spike Go!
https://arxiv.org/ftp/arxiv/papers/2105/2105.05956.pdf
The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.
Any dots in this group of trained neural nuts? Lots of names and neuro-job-titles.
Only 169 pages to read. Let me summarize: "you guessed it", proclaiming Go Spike Go!
https://arxiv.org/ftp/arxiv/papers/2105/2105.05956.pdf
The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.
Krustor
Regular
Whatever your post was: I completely understand the delete just by remembering your last posts...
Btw: Do you know the famous Bavarian Pumuckl from my childhood? This little guy always came to my mind when I had to read your content.
Attachments
Kachoo
Regular
I recall hearing one EAP choose not to proceed. I don't remember where I heard that from Sean but it was in a video I believe. I'm not 100% sure but that is what I believe I heard. Imo
Similar threads
