Yunping Zhao, Jianzhuang Lu*, Xiaowen Chen
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 517-535, 2021, DOI:10.32604/cmc.2020.012380
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… More >
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