Group Vectored Absolute-Value-Subtraction Cell Array for the Efficient Acceleration of AdderNet

Abstract

Convolutional neural networks (CNN) have been widely used for boosting the performance of Artificial Intelligence (AI) tasks. However, the CNN models are usually computational intensive. Recently, the novel absolute-value-subtraction (ABS) operation based CNN, namely the AdderNet is proposed to reduce the computation complexity and energy burden. But the specific hardware design has rarely been explored. In this work, we propose an energy-efficient AdderNet accelerator to address such issue. At the hardware architecture level, we develop a flexible and group vectored systolic array to balance the circuit area, power, and speed. Thanks to the low delay of ABS operation, the systolic array can reach extremely high frequency up to 2GHz. Meanwhile the power- and area- efficiency exhibits about 3× improvement compared with its CNN counterpart. At the processing element level, we propose new ABS cell based on algorithm optimization, which shows about 10% higher performance than the naive design. Finally, the accelerator is practically deployed on FPGA platform to accelerate the AdderNet ResNet-18 network as a case study. The peak throughput is 424.2 GOP/s, which is much higher than previous works.