In this work we devise and train a RRAM-based low-precision neural network with binary weights and 4-bits activations. Full-circuit simulations including the analog neuron peripheral circuitry are run in different conditions, including the effect of RRAM devices nonidealities, to evaluate the reliability and performance of the network when executing a classification task. Results show that the power-throughput trade-off during inference is governed by the neuron circuitry, and that the reset conditions can be tuned to simultaneously maximize energy efficiency and accuracy leading to improved network reliability. Accuracy losses are found to be dominated by the variability of the RRAMs in low resistive state (LRS), which suggests specific strategies for accuracy loss minimization. The network shows excellent performance in terms of accuracy, throughput, and energy efficiency, with robustness to RRAM non-idealities.

Performances and Trade-offs of Low-Bit Precision Neural Networks based on Resistive Memories / Zanotti, T.; Pavan, P.; Puglisi, F. M.. - 2021-:(2021), pp. 7-11. ((Intervento presentato al convegno 2021 IEEE International Integrated Reliability Workshop, IIRW 2021 tenutosi a usa nel 2021 [10.1109/IIRW53245.2021.9635626].

Performances and Trade-offs of Low-Bit Precision Neural Networks based on Resistive Memories

Zanotti T.;Pavan P.;Puglisi F. M.
2021

Abstract

In this work we devise and train a RRAM-based low-precision neural network with binary weights and 4-bits activations. Full-circuit simulations including the analog neuron peripheral circuitry are run in different conditions, including the effect of RRAM devices nonidealities, to evaluate the reliability and performance of the network when executing a classification task. Results show that the power-throughput trade-off during inference is governed by the neuron circuitry, and that the reset conditions can be tuned to simultaneously maximize energy efficiency and accuracy leading to improved network reliability. Accuracy losses are found to be dominated by the variability of the RRAMs in low resistive state (LRS), which suggests specific strategies for accuracy loss minimization. The network shows excellent performance in terms of accuracy, throughput, and energy efficiency, with robustness to RRAM non-idealities.
2021 IEEE International Integrated Reliability Workshop, IIRW 2021
usa
2021
2021-
7
11
Zanotti, T.; Pavan, P.; Puglisi, F. M.
Performances and Trade-offs of Low-Bit Precision Neural Networks based on Resistive Memories / Zanotti, T.; Pavan, P.; Puglisi, F. M.. - 2021-:(2021), pp. 7-11. ((Intervento presentato al convegno 2021 IEEE International Integrated Reliability Workshop, IIRW 2021 tenutosi a usa nel 2021 [10.1109/IIRW53245.2021.9635626].
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1260080
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact