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EnglishTemporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series analysis. We introduce an automated exploration approach and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power (PULP) microcontrollers. Our approach minimizes latency and energy by exploiting a layer tiling optimizer to jointly find the tiling dimensions and select among alternative implementations of the causal and dilated 1D-convolution operations at the core of TCNs. We benchmark our approach on a commercial PULP device, achieving up to 103 imes lower latency and 20.3 imes lower energy than the Cube-AI toolkit executed on the STM32L4 and from 2.9 imes to 26.6 imes lower energy compared to commercial closed-source and academic open-source approaches on the same hardware target.
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference / Burrello, Alessio; Dequino, Alberto; Jahier Pagliari, Daniele; Conti, Franceso; Zanghieri, Marcello; Macii, Enrico; Benini, Luca; Poncino, Massimo. - ELETTRONICO. - 2021:(2021), pp. 1-6. ((Intervento presentato al convegno 2021 IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2021 tenutosi a USA nel 2021 [10.1109/ISLPED52811.2021.9502494].
| Titolo: | TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Abstract: | Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Ser...ies analysis. We introduce an automated exploration approach and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power (PULP) microcontrollers. Our approach minimizes latency and energy by exploiting a layer tiling optimizer to jointly find the tiling dimensions and select among alternative implementations of the causal and dilated 1D-convolution operations at the core of TCNs. We benchmark our approach on a commercial PULP device, achieving up to 103 imes lower latency and 20.3 imes lower energy than the Cube-AI toolkit executed on the STM32L4 and from 2.9 imes to 26.6 imes lower energy compared to commercial closed-source and academic open-source approaches on the same hardware target. | |
| ISBN: | 978-1-6654-3922-0 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| ISPLED21___TCN_Library.pdf | Articolo principale (post-print) | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| TCN_Mapping_Optimization_for_Ultra-Low_Power_Time-Series_Edge_Inference.pdf | Articolo principale (versione editoriale) | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2924896
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