Quantization and sparsity-aware processing for energy-efficient NVM-based convolutional neural networks

Abstract

Nonvolatile memory (NVM)-based convolutional neural networks (NvCNNs) have received widespread attention as a promising solution for hardware edge intelligence. However, there still exist many challenges in the resource-constrained conditions, such as the limitations of the hardware precision and cost and, especially, the large overhead of the analog-to-digital converters (ADCs). In this study, we systematically analyze the performance of NvCNNs and the hardware restrictions with quantization in both weight and activation and propose the corresponding requirements of NVM devices and peripheral circuits for multiply–accumulate (MAC) units. In addition, we put forward an in situ sparsity-aware processing method that exploits the sparsity of the network and the device array characteristics to further improve the energy efficiency of quantized NvCNNs. Our results suggest that the 4-bit-weight and 3-bit-activation (W4A3) design demonstrates the optimal compromise between the network performance and hardware overhead, achieving 98.82% accuracy for the Modified National Institute of Standards and Technology database (MNIST) classification task. Moreover, higher-precision designs will claim more restrictive requirements for hardware nonidealities including the variations of NVM devices and the nonlinearities of the converters. Moreover, the sparsity-aware processing method can obtain 79%/53% ADC energy reduction and 2.98×/1.15× energy efficiency improvement based on the W8A8/W4A3 quantization design with an array size of 128 × 128.