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EnglishThe portability of Convolutional Neural Networks (ConvNets) on the mobile edge of the Internet has proven extremely challenging. Embedded CPUs commonly adopted on portable devices were designed and optimized for different kinds of applications, hence they suffer high latency when dealing with the parallel workload of ConvNets. Reduction techniques playing at the algorithmic level are viable options to improve performance, e.g. topology optimization using alternative forms of convolution and arithmetic relaxation via fixed-point quantization. However, their efficacy is hardware sensitive. This paper provides an overview of these issues using as a case study an image classification task implemented through open-source resources, namely different architectures of MobileNet (vl), scaled, trained and quantized for the ImageNet dataset. In this work, we quantify the accuracy-performance trade-off on a commercial board hosting an ARM Cortex-A big. LITTLE system-on-chip. Experimental results reveal mismatches which arise from the hardware.
Inference on the Edge: Performance Analysis of an Image Classification Task Using Off-The-Shelf CPUs and Open-Source ConvNets / Peluso, V.; Rizzo, R. G.; Cipolletta, A.; Calimera, A.. - (2019), pp. 454-459. ((Intervento presentato al convegno 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019 tenutosi a esp nel 2019.
| Titolo: | Inference on the Edge: Performance Analysis of an Image Classification Task Using Off-The-Shelf CPUs and Open-Source ConvNets |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | The portability of Convolutional Neural Networks (ConvNets) on the mobile edge of the Internet ha...s proven extremely challenging. Embedded CPUs commonly adopted on portable devices were designed and optimized for different kinds of applications, hence they suffer high latency when dealing with the parallel workload of ConvNets. Reduction techniques playing at the algorithmic level are viable options to improve performance, e.g. topology optimization using alternative forms of convolution and arithmetic relaxation via fixed-point quantization. However, their efficacy is hardware sensitive. This paper provides an overview of these issues using as a case study an image classification task implemented through open-source resources, namely different architectures of MobileNet (vl), scaled, trained and quantized for the ImageNet dataset. In this work, we quantify the accuracy-performance trade-off on a commercial board hosting an ARM Cortex-A big. LITTLE system-on-chip. Experimental results reveal mismatches which arise from the hardware. |
| ISBN: | 978-1-7281-2946-4 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| SNAMS19.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri | |
| 08931889_snam.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2816982
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