Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866916534423650304 |
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| author | Saeedi, Sepide Carpegna, Alessio Savino, Alessandro Di Carlo, Stefano |
| author_facet | Saeedi, Sepide Carpegna, Alessio Savino, Alessandro Di Carlo, Stefano |
| contents | Approximate Computing (AxC) techniques trade off the computation accuracy for performance, energy, and area reduction gains. The trade-off is particularly convenient when the applications are intrinsically tolerant to some accuracy loss, as in the Spiking Neural Networks (SNNs) case. SNNs are a practical choice when the target hardware reaches the edge of computing, but this requires some area minimization strategies. In this work, we employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error to detect when the approximation exceeds tolerable limits by the application. Experimental results confirm the capability of reducing the exploration time significantly, providing the chance to reduce the network parameters' size further and with more fine-grained results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_11782 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks Saeedi, Sepide Carpegna, Alessio Savino, Alessandro Di Carlo, Stefano Neural and Evolutionary Computing Approximate Computing (AxC) techniques trade off the computation accuracy for performance, energy, and area reduction gains. The trade-off is particularly convenient when the applications are intrinsically tolerant to some accuracy loss, as in the Spiking Neural Networks (SNNs) case. SNNs are a practical choice when the target hardware reaches the edge of computing, but this requires some area minimization strategies. In this work, we employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error to detect when the approximation exceeds tolerable limits by the application. Experimental results confirm the capability of reducing the exploration time significantly, providing the chance to reduce the network parameters' size further and with more fine-grained results. |
| title | Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2212.11782 |