Temporal 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 A.;Dequino A.;Conti F.;Zanghieri M.;Benini L.;
2021

Abstract

Temporal 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.
Proceedings of the International Symposium on Low Power Electronics and Design
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Burrello A.; Dequino A.; Pagliari D.J.; Conti F.; Zanghieri M.; MacIi E.; Benini L.; Poncino M.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/847018
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