Evolvable Hyperdimensional Computing: Unsupervised Regeneration of Associative Memory to Recover Faulty Components
- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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 4.2 MB of text for regeneration. The new evolved model is still 285× more compact than state-of-the-art fastText. Show more
Book title2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Pages / Article No.
SubjectEvolvable hardware; HD computing; PCM
Organisational unit03996 - Benini, Luca / Benini, Luca
780215 - Computation-in-memory architecture based on resistive devices (EC)
NotesConference postponed due to Corona virus (COVID-19). Due to the Corona virus (COVID-19) the conference was conducted virtually.
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