NR-MPA: Non-Recovery Compression Based Multi-Path Packet-Connected-Circuit Architecture of Convolution Neural Networks Accelerator

Convolution Neural Networks (CNNs) involve massive data to be calculated and stored. To meet the challenges above, parallel hardware accelerators consisting of hundreds of Processing Elements (PEs) arranged as a many-core systemon-chip, connected by a Network-on-Chip (NoC) are proposed, which achieve high throughput exploiting parallel PE array. However, most of existing accelerators focus on only one aspect, such as compute structure of PE and data movement overhead above NoC, which causes the throughout, area and latency of the accelerator not fully optimized. In this paper, we propose an efficient general purpose CNN accelerator including both compute based on Non-Recovery Compression (NRC) method and data movement by novel Multi-Paths Packet Connection Circuit (MP-PCC). NRC can save computation time due to zero multiplier through shift decoding in PE and improve power efficiency by saving a large number of data transmission. MPPCC, evolved from Packet Connection Circuit, supports single and multicast transmission modes at the same time, and changes the multicast (X, Y) routing algorithm to multicast Y algorithm to improve the transmission efficiency. The proposed architecture which was implemented on Xilinx FPGA achieves 17.7x faster computation speed and 2.2x fewer memory accesses compared with the state-of-the-art method.