AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators
Overview & Synopsis
An adaptive inference method for crossbar (AIDX) is presented based on an optimization scheme for adjusting the duration and amplitude of input voltage pulses. AIDX minimizes the long-term effects of memristance drift on artificial neural network accuracy. The sub-threshold behavior of memristor has been modeled and verified by comparing with fabricated device data. The proposed method has been evaluated by testing on different network structures and applications, e.g., image reconstruction and classification tasks. The results showed an average of 60% improvement in convolutional neural network (CNN) performance on CIFAR10 dataset after 10000 inference operations as well as a 78.6% error reduction in image reconstruction
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Document Information
| Document Title | AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators |
|---|---|
| Author / Organization | Tony Liu, Amirali Amirsoleimani, F. Alibart, Serge Ecoffey, Dominique Drouin, Roman Genov |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2021 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-238379 |
| Original Source Link | Visit Source Page on hal.science » |