Open Access

ARTICLE

A Dynamically Reconfigurable Accelerator Design Using a Sparse-Winograd Decomposition Algorithm for CNNs

by Yunping Zhao, Jianzhuang Lu^*, Xiaowen Chen

National University of Defence Technology, Changsha, China

* Corresponding Author: Jianzhuang Lu. Email:

Computers, Materials & Continua 2021, 66(1), 517-535. https://doi.org/10.32604/cmc.2020.012380

Received 28 June 2020; Accepted 25 July 2020; Issue published 30 October 2020

Abstract

Convolutional Neural Networks (CNNs) are widely used in many fields. Due to their high throughput and high level of computing characteristics, however, an increasing number of researchers are focusing on how to improve the computational efficiency, hardware utilization, or flexibility of CNN hardware accelerators. Accordingly, this paper proposes a dynamically reconfigurable accelerator architecture that implements a Sparse-Winograd F(2 2.3 3)-based high-parallelism hardware architecture. This approach not only eliminates the pre-calculation complexity associated with the Winograd algorithm, thereby reducing the difficulty of hardware implementation, but also greatly improves the flexibility of the hardware; as a result, the accelerator can realize the calculation of Conventional Convolution, Grouped Convolution (GCONV) or Depthwise Separable Convolution (DSC) using the same hardware architecture. Our experimental results show that the accelerator achieves a 3x–4.14x speedup compared with the designs that do not use the acceleration algorithm on VGG-16 and MobileNet V1. Moreover, compared with previous designs using the traditional Winograd algorithm, the accelerator design achieves 1.4x–1.8x speedup. At the same time, the efficiency of the multiplier improves by up to 142%.

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