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Journal Articles IEEE Transactions on Circuits and Systems I: Regular Papers Year : 2018

A fully flexible circuit implementation of clique-based neural networks in 65-nm CMOS

Abstract

Clique-based neural networks implement low-complexity functions working with a reduced connectivity between neurons. Thus, they address very specific applications operating with a very low-energy budget. However, the implementation in the state of the art is not flexible and a fabricated circuit is only usable in a unique use case. Besides, the silicon area of hardwired circuits grows exponentially with the number of implemented neurons that is prohibitive for embedded applications. This paper proposes a flexible and iterative neural architecture capable of implementing multiple types of clique-based neural networks of up to 3968 neurons. The circuit has been integrated in an ST 65-nm CMOS ASIC and occupies a 0.21-mm 2 silicon surface area. The proper functioning of the circuit is illustrated using two application cases: a keyword recovery application and an electrocardiogram classification. The neurons outputs are updated 83 ns after a stimulation, and a neuron needs an energy of 115 fJ to propagate a change at the input to its output.
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Dates and versions

hal-01983523 , version 1 (16-01-2019)

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Benoit Larras, Paul Chollet, Cyril Lahuec, Fabrice Seguin, Matthieu Arzel. A fully flexible circuit implementation of clique-based neural networks in 65-nm CMOS: [Invited]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2018, 66 (5), pp.1-12. ⟨10.1109/TCSI.2018.2881508⟩. ⟨hal-01983523⟩
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