Neuromorphic: brainlike computing, page-16

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08/09/20

15:40

Post #: 47178236
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Originally posted by uiux: ↑

Just quickly look in scholar, notice the difference however:

Weebit research published ~April this year, still working on MNIST....
vs
Brainchip has a development environment that lets you install and start building products with their library in under 2 minutes...

Weebit Nano

Fully-Integrated Spiking Neural Network Using SiOx-Based RRAM as Synaptic Devicehttps://ieeexplore.ieee.org/abstract/document/9073840
Date Added to IEEE Xplore: 23 April 2020
Abstract:
This paper presents, to the best of the authors' knowledge, the first complete integration of a Spiking Neural Network combining analog neurons and SiO x -based resistive memory (RRAM) synapses. The implemented topology is a perceptron, and the circuit is aimed at performing MNIST digits classification. An existing framework was adapted for off-line learning and weight quantization, and the network was later converted into its spiking equivalent. The test chip, fabricated in 130 nm CMOS, shows a classification accuracy of 82%, with a 180 pJ energy dissipation per spike

Brainchip

This tutorial illustrates how to use a pre-trained Akida model to process the MNIST dataset.
https://doc.brainchipinc.com/examples/plot_gxnor_mnist.html
Depending on the number of samples you run, you should find a performance of around 99% (99.35% if you run all 10000 samples).

That should give you a very clear comparison between the two

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"a Spiking Neural Network combining analog neurons and SiO x -based resistive memory (RRAM) synapses. "
If memory serves, PvdM mentioned that analog can't be implemented in COMS.
Can't be implemented in 7 nm because of voltage linitations.
Also I think analog may be less acccurate than digital.

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4 Upvote

0 Great analysis

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