Deep Convolutional Neural Network Accelerator Featuring Conditional Computing and Low External Memory Access

Abstract

This paper presents an ASIC accelerator for deep convolutional neural networks (DCNNs) featuring a novel conditional computing scheme that synergistically combines precision-cascading with zero-skipping. To reduce many redundant convolution operations that are followed by max-pooling operations, we propose precision-cascading, where the input features are divided into a number of low-precision groups and approximate convolutions with only the most significant bits (MSBs) are performed first. Based on this approximate computation, the full-precision convolution is performed only on the maximum pooling output that is found. This way, the total number of bit-wise convolutions can be reduced by ~2× without affecting the output feature values and with <0.8% degradation in final ImageNet classification accuracy. Precision-cascading provides the added benefit of increased sparsity per low-precision group, which we exploit with zero-skipping to eliminate clock cycles as well as external memory access that involve zero inputs. By jointly optimizing the conditional computing scheme and hardware architecture, the 40nm prototype chip demonstrates a peak energy-efficiency of 8.85 TOPS/W at 0.9V supply and low external memory access of 55.31 MB (or 0.0018 access/MAC) for ImageNet classification with VGG-16 CNN.