EA-HWP: An Efficient CNN/SNN Accelerator With Hybrid Weight Precision

Due to the low power consumption and event-driven characteristics, spiking neural networks (SNNs) have garnered significant interest in recent years. In comparison, convolutional neural networks (CNNs) achieve state-of-the-art results in image classification tasks by virtue of their local perception and hierarchical learning characteristics. The fusion computing of SNN and CNN represents a promising direction that can fully leverage the low-power advantages of SNN and the high-precision advantages of CNN. In this paper, a 28-nm efficient heterogeneous CNN/SNN accelerator, named EA-HWP is proposed. EA-HWP employs a heterogeneous distributed design for the efficient on-chip implementation of CNN computation, CNN-SNN conversion, and SNN computation. Meanwhile, based on the characteristics of weight distribution, dense-and-sparse computing architecture is proposed for hybrid weight precision computation. Fabricated in 28-nm CMOS process, EA-HWP can run at a maximum of 1152MHz for CNN and 792MHz for SNN. When the supply voltage is 0.625V, EA-HWP can achieve energy efficiency of 0.14pJ/OP for CNN and 0.26pJ/SOP for SNN, respectively.