Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited)

Abstract

Analog non-volatile memory (NVM)-based accelerators for Deep Neural Networks (DNNs) can achieve high-throughput and energy-efficient multiply-accumulate (MAC) operations by taking advantage of massively parallelized analog compute, implemented with Ohm's law and Kirchhoff's current law on arrays of resistive memory devices. Competitive end-to-end DNN accuracies can be obtained, provided that weights are accurately programmed onto NVM devices and MAC operations are sufficiently linear. In this paper, we report architectural and circuit advances for such Analog NVM-based accelerators. We describe a highly heterogeneous and programmable accelerator architecture for DNN inference that combines analog NVM memory-array 'Tiles' for weight-stationary, energy-efficient MAC operations, together with heterogeneous special-function Compute-Cores for auxiliary digital computation. Massively parallel vectors of neuron-activation data are exchanged over short distances using a dense and efficient circuit-switched 2D mesh, enabling a wide range of DNN workloads, including CNNs, LSTMs, and Transformers. We also show a 14-nm inference chip consisting of multiple 512 × 512 arrays of Phase Change Memory (PCM) devices which implements multiple DNN benchmarks using such a circuit-switched 2D mesh.