A 1.52 pJ/Spike Reconfigurable Multimodal Integrate-and-Fire Neuron Array Transceiver

Abstract

An ultra-low power integrate-and-fire neuron array transceiver with a multi-modal neuron architecture is presented. The design features an array of 16×16 charge-mode mixed-signal neurons that can be configured to implement a variety of activation functions, including step, sigmoid and Rectified Linear Unit (ReLU), through reconfiguration of clocking waveforms through partial reset in charge accumulation and additive stochastic noise by Linear Feedback Shift Register (LFSR) coupling. The neuron outputs spike-based sparse synchronous events, which are either binary (event/no event) or ternary (positive/negative/no events). The reconfigurable energyefficient design makes this architecture suitable for deep learning and neuromorphic applications like Restricted Boltzmann Machines, Convolutional Neural Networks and general event-driven computing. The 1.796 mm 2 chip fabricated in 130nm CMOS technology consumes 140.6 µW from a 1.8V supply at 92.5 MSpikes/s achieving an energy efficiency Figure-of-Merit (FoM) of 1.52 pJ/Spike. A CNN architecture implemented on the chip using sigmoid and ReLU activation achieves MNIST prediction accuracy of 94.8% and 96.9%.