Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks

TL;DR

This paper introduces a novel computation flow and data format for hardware acceleration of CNNs, significantly reducing storage needs and improving processing efficiency without sacrificing accuracy.

Contribution

It proposes the stacked filters stationary flow (SFS), a new data encoding format (CSF), and a 3D-SIMD processor architecture to enhance CNN acceleration on hardware.

Findings

Achieves 1.11x and 1.09x storage reduction for AlexNet and SqueezeNet.

Improves PE array utilization rate to 96.5%.

Reduces chip area for handling sparse data.

Abstract

To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding format, relative indexed compressed sparse filter format (CSF), to make the best of data sparsity, and simplify data handling at execution time. And we also propose a three dimensional Single Instruction Multiple Data (3D-SIMD) processor architecture to illustrate how to accelerate deep CNNs by taking advantage of SFS flow and CSF format. Comparing with the state-of-the-art result (Han et al., 2016b), our methods achieve 1.11x improvement in reducing the storage required by AlexNet, and 1.09x improvement in reducing the storage required by SqueezeNet, without loss of accuracy on the ImageNet dataset. Moreover, using these approaches, chip area for logics handling irregular sparse data access can be saved. Comparing with the 2D-SIMD processor structures in DVAS, ENVISION, etc., our methods achieve about 3.65x processing element (PE) array utilization rate improvement (from 26.4\% to 96.5\%) on the data from Deep Compression on AlexNet.