A Noise-Resilient Neuromorphic Digit Classifier Based on NOR Flash Memories with Pulse–Width Modulation Scheme
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 classi...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/67e11673a39745f2bf3d6269ba7356f6 |
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| Sumario: | 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>V</mi><mi>T</mi></msub></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>V</mi><mi>T</mi></msub></semantics></math></inline-formula> 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. |
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