Hi Baisyet,@Diogenese @uiux you guys might be able to help us understand on below which was posted on LinkedIn as one of the guy coment on BrB and the answer below got me confused.
Peter Sillan you bring up good points, and highlight the key gap to realizing this groundswell : a lack of available, usable, interoperable hashtag#neuromorphic Silicon
But are you sure BrainChip is neuromorphic? To date only their marketing material says so. In disclosures at NICE, ICONS last year their CTO opened the kimono a little, and conceded that they’re really just an Event-based General Matrix Engine (ie more CNN than SNN) pipeline - although not sure if he still has CTO role, he was a new hire trying to be more transparent.
To be clear : event-based is a forward step, but maybe one of the smallest pieces of the key Neuromorphic advances, but the published brainchip patents only show changes that are more incremental, than revolutionary. And even for that, they’ve had to “roll their own” TENN. So it’s clearly an uphill battle to do just this, as a one-off chip maker, solo solution.
But if intellectually honest, just event-based but still systolic array fmac engine, is not a big differentiation from today’s GPU/TPU/AI chip arch.
An independent view of the landscape I would refer you to Catherine Schuman’s excellent paper
It looks like the writer has not delved too deeply into the workings of Akida beyond the BrainCHip marketing material, and has limited understanding of its architecture or functionality. .
A couple of things:
A. Some people reserve the term "neuromorphic" for analog neurons, whereas Akida is digital.
Akida implements the functionality of real neurons in a digital embodiment which eliminates the manufacturing reproducability problems inherent in analog implementations.
Analog neurons more closely imitate the electric function of real neurons by accumulating current from a plurality ofconnected neurons in a capacitor.
Live neurons accumulate electric charges from other neurons until a firing threshold is reached.
Bias and weight are determined by the Model (the reference database of images, words, …)
Activations are the input spikes/events.
MemRistors/ReRAM are used in other forms of analog SNNs.
The Zener diode blocks current until the input voltage across the capacitor exceeds the Zener threshold voltage.
Due to manufacturing variability, the accuracy of analog neurons causes significant errors, and any analog neuron system needs some form of error correction.
Akida is digital, not analog, so it does not have this problem.
B. Akida's "event based" NN is an enhanced spiking NN.
Akida initially operated with 1-bit weights and activations, with the single bit corresponding to a "spike". However early access programme (EAP) feedback indicated a need for greater precision, so the production version of Akida 1 was adapted to handle 1, 2, or 4-bit activations and weights.
This is a patent which illustrates Akida's digital neuron:
WO2020092691A1 AN IMPROVED SPIKING NEURAL NETWORK
The term "event" thus encompasses both single bit and multi-bit weights/acivations. Adding multi-bit capability has increased the versatility of Akida 1. Choosing the 1-bit mode provides both minimal latency and minimal power consumption - ideal for watchhdog/wake applications and other applications whichdo not demand high precision. The 4-bit mode provides much greater precision, at the expense of increased power consumption.
Akida 2 has further enhanced the range of applications by inluding 8-bit input activations and weights, and a 16-bit implementation has been foreshadowed.
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