tinyML EMEA – Lars Keuninckx: Monostable Multivibrator Networks: extremely low power inference…

Monostable Multivibrator Networks: extremely low power inference at the edge with timer neurons
Lars KEUNINCKX
Researcher
imec

In our proposed presentation, we will discuss the following:
• MMV introduction and how MMV networks set up and test spike timing conditions,
• the training algorithm, which is responsible for optimizing the excitatory and inhibitory input and recurrent connections of the OR-ing network, as well as the integer periods of the MMVs. Our method is based on the surrogate gradient technique and slow binarization of the connections,
• several use cases on publicly available datasets: Google Soli radar gestures, Heidelberg keyword spotting, IBM DVS-128 gestures and the Yin-Yang symbol segmentation, all with excellent results.
• future work and outlook.

A general tenet of neuromorphic engineering is that taking inspiration from biological reality will naturally lead to the most efficient hardware possible. We argue that an overemphasis on the biological apparatus could become self-limiting for the field since the instrumental biological operating principles, even if understood well enough in detail, may simply not be transferable to the electronic hardware domain. After all, neurons are living cells first-with all their idiosyncrasies- and only then computational units. Thus, we reverse the original neuromorphic question: instead of trying to find ways to efficiently implement and network a biological neuron in electronic hardware, we ask which are the fundamental electronic building blocks that are easy to implement and connect en masse that we already have at our disposal and what are their computational properties.
As a possible answer to this question, we present networks of monostable multivibrator (MMVs).
MMVs are simple timers that are straightforward to implement using counters in digital hardware.

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