D
Deleted member 118
Guest
Does anyone have any broker detail of late as it be interesting to see who has been selling of late, which has seemed to of subsided today.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Rskiff
Regular
Thanks for the reply Tech. How about getting on the "Bat phone, or Bat email" to inform the individual you think may read your post, then guaranteed info is passed on. thanks
Fullmoonfever
Top 20
Recent chip conference in China.
Predominantly around Intel in the article but was the paragraph below that caught my eye.
Err...hello?
In addition to heterogeneous computing and heterogeneous integration, the exploration of neuromorphic chips is also an important way to break the bottleneck of computing power.
Song Jiqiang pointed out that most of the current artificial intelligence still relies on GPU, CPU or accelerator, and their essence is still the price drop of multiplier-accumulator. The neuromorphic chip constructs the underlying computing unit in a way that simulates human neurons.
If such a chip is constructed, and artificial intelligence algorithms are programmed by means of a spiking neural network, it will have the opportunity to achieve a thousand-fold improvement in energy efficiency. Under the same energy consumption, it can reach a thousand times the computing power of the current multiplier-adder chip.
Predominantly around Intel in the article but was the paragraph below that caught my eye.
Err...hello?
In addition to heterogeneous computing and heterogeneous integration, the exploration of neuromorphic chips is also an important way to break the bottleneck of computing power.
Song Jiqiang pointed out that most of the current artificial intelligence still relies on GPU, CPU or accelerator, and their essence is still the price drop of multiplier-accumulator. The neuromorphic chip constructs the underlying computing unit in a way that simulates human neurons.
If such a chip is constructed, and artificial intelligence algorithms are programmed by means of a spiking neural network, it will have the opportunity to achieve a thousand-fold improvement in energy efficiency. Under the same energy consumption, it can reach a thousand times the computing power of the current multiplier-adder chip.
FabricatedLunacy
Regular
Looks like I’m getting another cheap top up end of month when another work bonus hits!
Boab
I wish I could paint like Vincent
A little more info for those that may have missed it. Should be a cracker.
Learn How to Develop Neuromorphic ML Models Running BrainChip Akida
Join Edge Impulse and BrainChip to learn about the the power of neuromorphic computing
www.edgeimpulse.com
Show's how good Anil is.
buena suerte :-)
BOB Bank of Brainchip
Thanks Boab .... looking forward to it ..A little more info for those that may have missed it. Should be a cracker.
Learn How to Develop Neuromorphic ML Models Running BrainChip Akida
Join Edge Impulse and BrainChip to learn about the the power of neuromorphic computing
robsmark
Regular
My earlier post regarding a conversation I had with Tony a couple of weeks ago was deleted after Tony refuted some of the comments which I made.
I can assure all that the post was made with no bad intentions, and that the conversation was relayed to the best of my ability. As I mentioned in the post - no new information was discussed/released by Tony.
Tony - my apologies if I misquoted or misrepresented you in any way.
I can assure all that the post was made with no bad intentions, and that the conversation was relayed to the best of my ability. As I mentioned in the post - no new information was discussed/released by Tony.
Tony - my apologies if I misquoted or misrepresented you in any way.
alwaysgreen
Top 20
Well today sucks...
Hopefully onwards and upwards for the remainder of the week.
Hopefully onwards and upwards for the remainder of the week.
SERA2g
Founding Member
Lesson learned here for everyone. If you're going to quote a 'personal' conversation you've had with anyone from the company it's probably best to check in with the person in question before sharing it here.
Appreciate your efforts though @robsmark, I'm positive your intentions were good!
Cheers mate
Vojnovic
Regular
Germans continue their love affair with our company:
www.esg-aktien.de
Gamechanger für Chip-Hersteller: Infineon, BYD, BrainChip, Volkswagen
Computer-Chips bleiben gefragt. Wie der deutsche Hersteller Infineon kürzlich im Handelsblatt mitteilte, plane das Unternehmen gemeinsam mit Kunden Lieferungen mit einer Vorlaufzeit von rund zwei Jahren und mehr. Wie die Zeitung schätzt, sei diese Marktsituation für Investoren günstig und sorge...
equanimous
Norse clairvoyant shapeshifter goddess
Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram
arxiv.org
Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature...
TheFunkMachine
seeds have the potential to become trees.
But Akida is not a sensor rather sensor agnostic, or neuromorphic processor that process sensor input/output.
Not saying it’s not Akida, I’m just saying Akida is not a neuromorphic sensor
Sirod69
bavarian girl ;-)
This is an interesting paper. Does not name BRN or AKIDA but when read you will see why partnering with ARM Cortex M4 is so very exciting:
A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor
....System-On-Chip (SoC) devices are an attractive solution that, in addition to high processing capabilities, includes multiple peripheral devices that can be very helpful for the sophisticated requirements of deep-learning applications. Examples of manufacturers that develop AI integrated circuits for edge computing are Samsung, Texas Instruments, Qualcomm, and STM. Some of their recent products are briefly presented below.......
5. Conclusions
Deep learning and deep neural networks are emerging as promising solutions for solving complex problems. Solving complex problems requires high computational capabilities and memory resources, so are traditionally designed to run on a large computer system around specialized hardware. However, recent research shows that simple applications can benefit from the deep learning paradigm and their edge computing implementation as well. Edge computing is the solution to many real-world problems that need to be solved soon. For instance, the automotive industry is using and developing prototypes using state-of-the-art hardware and software solutions for autonomous driving. Once these prototypes prove their ability to solve problems, the systems will have to run on real-world cars. At that stage, cost is necessary to be competitive in the market, and, using high performance computing solutions, the cost is high. The edge computing paradigm must be prepared with efficient and low-cost solutions while meeting specific requirements such as functional safety. In this work, we provide a summary of what edge computing means in the context of low-cost/low-power applications. Here, the ARM Cortex-M processor represents one of the best possible candidates. More specifically, we summarize deep neural network implementations using ARM Cortex-M core-based microcontrollers. From the software perspective, the STM32Cube.AI support package, made available by STMicroelectronics for its 32-bit microcontroller series, represents one of the best freely available tools. Implementing deep neural networks on embedded devices, such as microcontrollers, is a difficult task. This is mainly due to the computation and memory footprint constrains. For this reason, it is observed that developers are forced to customize existing architectures or even develop from scratch innovative models that better suit embedded processors. Optimization techniques such as quantization, pruning, and distillation are constantly evolving to achieve higher performance, and they are enabling developers to introduce state-of-the-art models of increasing complexity to the embedded domain. Ultimately, using an optimized hardware combined with optimized deep neural network architectures leads to maximum energy efficient systems. Electronics 2022, 11, 2545 19 of 21 Future work proposes to extend the study to a wider family of ARM cores, including, for example, deep learning applications running on Cortex-A type processors or even specialized Arm Ethos-N series processors for machine learning
https://www.mdpi.com/2079-9292/11/16/2545/pdf?version=1660467458
A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor
....System-On-Chip (SoC) devices are an attractive solution that, in addition to high processing capabilities, includes multiple peripheral devices that can be very helpful for the sophisticated requirements of deep-learning applications. Examples of manufacturers that develop AI integrated circuits for edge computing are Samsung, Texas Instruments, Qualcomm, and STM. Some of their recent products are briefly presented below.......
5. Conclusions
Deep learning and deep neural networks are emerging as promising solutions for solving complex problems. Solving complex problems requires high computational capabilities and memory resources, so are traditionally designed to run on a large computer system around specialized hardware. However, recent research shows that simple applications can benefit from the deep learning paradigm and their edge computing implementation as well. Edge computing is the solution to many real-world problems that need to be solved soon. For instance, the automotive industry is using and developing prototypes using state-of-the-art hardware and software solutions for autonomous driving. Once these prototypes prove their ability to solve problems, the systems will have to run on real-world cars. At that stage, cost is necessary to be competitive in the market, and, using high performance computing solutions, the cost is high. The edge computing paradigm must be prepared with efficient and low-cost solutions while meeting specific requirements such as functional safety. In this work, we provide a summary of what edge computing means in the context of low-cost/low-power applications. Here, the ARM Cortex-M processor represents one of the best possible candidates. More specifically, we summarize deep neural network implementations using ARM Cortex-M core-based microcontrollers. From the software perspective, the STM32Cube.AI support package, made available by STMicroelectronics for its 32-bit microcontroller series, represents one of the best freely available tools. Implementing deep neural networks on embedded devices, such as microcontrollers, is a difficult task. This is mainly due to the computation and memory footprint constrains. For this reason, it is observed that developers are forced to customize existing architectures or even develop from scratch innovative models that better suit embedded processors. Optimization techniques such as quantization, pruning, and distillation are constantly evolving to achieve higher performance, and they are enabling developers to introduce state-of-the-art models of increasing complexity to the embedded domain. Ultimately, using an optimized hardware combined with optimized deep neural network architectures leads to maximum energy efficient systems. Electronics 2022, 11, 2545 19 of 21 Future work proposes to extend the study to a wider family of ARM cores, including, for example, deep learning applications running on Cortex-A type processors or even specialized Arm Ethos-N series processors for machine learning
https://www.mdpi.com/2079-9292/11/16/2545/pdf?version=1660467458
How do you know this?
Are you referring to Akida?
projection
Member
^^ It was a 4ds reference
langeo
Regular
No, 4DS
They are talking about 4DS here.
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