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2021 BRN Discussion, page-3840

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26/03/21

17:27

Post #: 52073347
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Originally posted by EarlySession: ↑

interesting article from a German magazine that may have been shared but worth another share as has some key info from PVDM including:

- key points of differentiation (there are multiple)
- timeline impact of EAP
- akida price point
- akida power use benchmarks

Here’s a snippet for reference:

PVDM: We have therefore come to the conviction that the market for a pure SNN is simply not yet ripe, and we have therefore decided to integrate folding and pooling functions that run on the SNN, albeit with far less performance record as based on a MAC. It can be used as a front end that behaves like an event-based CNN, and as a back end that can learn autonomously. The user therefore has a choice because both options are combined. For this purpose we have developed the CNN2SNN converter, a software tool that is part of the Akida Development Environment. It enables users to develop their own networks and contains many examples of both CNNs and the actual SNNs.Did that cost a lot of additional chip space?Most importantly, it cost us an additional year of development time! But it was worth it to us because we are convinced that this is the only way to gain acceptance in the market. But to answer the question, no, the chip hasn't gotten much bigger because we're doing the convolution operations very efficiently. The SRAMs take up a relatively large part of the chip because there is always a capacity of 8 MB available on the chip to store the weighting values. Then there are the neurons, which take up most of the chip area.To do this, Brainchip developed the CNN to SNN Converter.That is the key, because the CNN2SNN converter makes the Akida unique in the group of neuromorphic chips. Both the Loihi from Intel and the Truenorth from IBM can neither perform convolution nor pooling, where the results of the convolution layers are aggregated. This is why no other neural network chip achieves such a low power consumption.Can you give an example of how the chip can learn independently of external training?The ability to learn on the chip is a key differentiator. To show this, we trained the chip to recognize cyber attacks. He was trained for 2.2 hours on the 202.8 Gb CSE IDS 2018 dataset. The SNN then recognized 98 percent of the attacks. In addition, the ability to learn on the chip can be combined with conventional deep learning. It can learn additional objects "on the fly", we call this incremental learning.

What price do users have to pay for the Akida chip?In large quantities, it will cost well under $ 20 for use in edge devices.Akida is on a completely different level. No one else combines an SNN with convolution and pooling.

Source: https://wfm-publish.blaetterkatalog.de/frontend/catalogs/155950/2/pdf/complete.pdf;jsessionid=444819C64B350053CA99B0654D6E58B6

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Congratulations ES into the Top Rated posts and will be there for light reading my interested parties tomorrow morning. Many thanks for finding this article.
FF.

BRN Price at posting: 63.5¢ Sentiment: Hold Disclosure: Held
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2021 BRN Discussion, page-3840

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2021 BRN Discussion, page-3840

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Buxton projects new graphite province discovery at WA Narryer play

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