Evolvable Hyperdimensional Computing: Unsupervised Regeneration of Associative Memory to Recover Faulty Components

Michael Hersche, Sara Sangalli, Luca Benini, Abbas Rahimi

Abstract: This paper proposes evolvable hyperdimensional (HD) computing to maintain high classification accuracy as permanent faults occur in emerging non-volatile memory fabrics. Our proposed HD architecture can detect, localize, and isolate faulty PCM blocks in discriminative classifiers, followed by unsupervised regeneration of new blocks to compensate accuracy loss. We demonstrate its application on a language recognition task: it is able to quickly relearn and fully recover the accuracy from 90.48% to 96.86% at fault rates as high as 42% by using solely 4. 2MB of text for regeneration. The new evolved model is still 285 more compact than state-of-the-art fastText.