| Autor: |
Sepide Saeedi, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 IEEE 23rd Latin American Test Symposium (LATS) |
| DOI: |
10.1109/lats57337.2022.9936999 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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