A Fully Flexible Circuit Implementation of Clique-Based Neural Networks in 65-nm CMOS

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-mm² 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.