A 1.52 pJ/Spike Reconfigurable Multimodal Integrate and Fire Neuron Array Transceiver
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 x 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 energy efficient 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 mm2 chip fabricated in 130nm CMOS technology consumes 140.6 mW 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%
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