AN-Coded Redundant Residue Number System for Reliable Neural Networks
Hsiao-Wen Fu, Ting‐Yu Chen, Cheng-Di Tsai, Meng-Wei Shen, Tsung‐Chu Huang · 2021
Residue Number Systems (RNS) can simultaneously improve computing acceleration, area reduction and power saving. For high-reliability applications like automotive electronics, partition-ability empowers the redundant RNS fault tolerance. However the parallel multiple modular redundancy will take a huge number of converters. This is the first paper to incorporate AN codes to the RRNS applied in high-reliable neural networks. From experimental results, the k-modulus redundancy can be reduced from 2 paths to only one, and the residue-to-binary converters can be saved from (k+2)(k+1) to only k+1 for the external-coding structure, and even to only one for the internal-coding structure in the single residue arithmetic-weight error correcting. From BLER simulations, about 130 times of reliability can be achieved for a 16-bit 47N-coded MAC.