A Noise-Resilient Neuromorphic Digit Classifier Based on NOR Flash Memories with Pulse-Width Modulation Scheme
| Autor: | Alessandro S. Spinelli, Gerardo Malavena, Christian Monzio Compagnoni |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK7800-8360
sezele Artificial neural network Computer Networks and Communications Computer science program noise neuromorphic computing Flash memory Noise Flash (photography) pulse-width modulation Neuromorphic engineering random telegraph noise Hardware and Architecture Control and Systems Engineering Modulation Signal Processing Classifier (linguistics) Electronic engineering NOR Flash memory arrays Electronics Electrical and Electronic Engineering artificial neural networks Pulse-width modulation |
| Zdroj: | Electronics Volume 10 Issue 22 Electronics, Vol 10, Iss 2784, p 2784 (2021) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics10222784 |
| Popis: | In this work, we investigate the implementation of a neuromorphic digit classifier based on NOR Flash memory arrays as artificial synaptic arrays and exploiting a pulse-width modulation (PWM) scheme. Its performance is compared in presence of various noise sources against what achieved when a classical pulse-amplitude modulation (PAM) scheme is employed. First, by modeling the cell threshold voltage (VT) placement affected by program noise during a program-and-verify scheme based on incremental step pulse programming (ISPP), we show that the classifier truthfulness degradation due to the limited program accuracy achieved in the PWM case is considerably lower than that obtained with the PAM approach. Then, a similar analysis is carried out to investigate the classifier behavior after program in presence of cell VT instabilities due to random telegraph noise (RTN) and to temperature variations, leading again to results in favor of the PWM approach. In light of these results, the present work suggests a viable solution to overcome some of the more serious reliability issues of NOR Flash-based artificial neural networks, paving the way to the implementation of highly-reliable, noise-resilient neuromorphic systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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