Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area reduction gains. One of the applications suitable for using AxC techniques are the Spiking Neural Networks (SNNs). SNNs are the new frontier for artificial intelligence since they allow for a more reliable hardware design. Unfortunately, this design requires some area minimization strategies when the target hardware reaches the edge of computing. In this work, we first extract the computation flow of an SNN, then employ Interval Arithmetic (IA) to model the propagation of the approximation error. This enables a quick evaluation of the impact of approximation. Experimental results confirm the model’s adherence and the capability of reducing the exploration time.

Prediction of the Impact of Approximate Computing on Spiking Neural Networks via Interval Arithmetic / Saeedi, Sepide; Carpegna, Alessio; Savino, Alessandro; Di Carlo, Stefano. - ELETTRONICO. - (2022), pp. 1-6. ((Intervento presentato al convegno 23rd IEEE Latin-American Test Symposium (LATS 2022) tenutosi a Virtual Event nel September 5 – September 7, 2022.

Prediction of the Impact of Approximate Computing on Spiking Neural Networks via Interval Arithmetic

Saeedi, Sepide;Carpegna, Alessio;Savino, Alessandro;Di Carlo, Stefano
2022

Abstract

Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area reduction gains. One of the applications suitable for using AxC techniques are the Spiking Neural Networks (SNNs). SNNs are the new frontier for artificial intelligence since they allow for a more reliable hardware design. Unfortunately, this design requires some area minimization strategies when the target hardware reaches the edge of computing. In this work, we first extract the computation flow of an SNN, then employ Interval Arithmetic (IA) to model the propagation of the approximation error. This enables a quick evaluation of the impact of approximation. Experimental results confirm the model’s adherence and the capability of reducing the exploration time.
File in questo prodotto:
File Dimensione Formato  
Paper___LATS22___IA_NN_Model.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 409.05 kB
Formato Adobe PDF
409.05 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2971542