面向投票类AI分类器的零冗余存储器容错设计OACSTPCD

Voting-based classifiers are widely used in many Artificial Intelligence(AI)applications.In their implementation,memories that store all known data are prone to suffer different effects like radiation and physical variations,causing soft errors and can even change the classification results.Therefore,error-tolerance must be achieved in these memories for safety-critical applications.Existing error-tolerant techniques commonly utilize error correction codes,however,the memory redundancy they introduce further increases the burden of storage.In this paper,a redundancy-free technique is proposed by focusing on the impact of errors on the classification perform-ance,instead of the error itself.It can recover the error-free classification results under errors by exploiting the flipped data.A k nearest neighbor classifier is taken as a case study to evaluate the proposed technique.The simulation results show that the proposed scheme of-fers almost full error tolerance without incurring any memory redundancy,moreover,it significantly reduces the hardware overheads for protection circuits compared to existing techniques.