A Compressed Spiking Neural Network Onto a Memcapacitive In-Memory Computing Array

Spiking neural networks (SNNs) enable the execution of deep learning-compatible tasks and approximation algorithms with low latency and low power consumption by operating on a neuromorphic system. Adopting analog in-memory computing (AiMC) in a neuromorphic system can build a system that has an advantage in memory density over a pure digital implementation. However, sensing the AiMC output with simple circuitry inevitably leads to unintended nonlinearities. In this study, we design a neuromorphic circuit using memcapacitive AiMC synapses with ultra-low power. We combine circuit nonlinearity-aware training (CNAT) with network compression techniques to prevent the SNN from losing accuracy caused by the neuron circuit’s nonlinearity and the synapse’s low resolution. The training runs on a machine learning framework and does not need to incorporate computationally intensive SPICE simulations. As simulated, our circuit performs MNIST classifications with almost no loss from ideal accuracy (97.64%) and consumes 15.7 nJ per inference.