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arXiv · 2203.12925

TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference

Alessio Burrello·Alberto Dequino·Daniele Jahier Pagliari·Francesco Conti·Marcello Zanghieri·Enrico Macii·Luca Benini·Massimo Poncino
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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 103X lower latency and 20.3X lower energy than the Cube-AI toolkit executed on the STM32L4 and from 2.9X to 26.6X lower energy compared to commercial closed-source and academic open-source approaches on the same hardware target.

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Alessio Burrello, Alberto Dequino, Daniele Jahier Pagliari, Francesco Conti, Marcello Zanghieri, Enrico Macii, Luca Benini, Massimo Poncino. 2022-03-24. TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference. https://doi.org/10.1109/islped52811.2021.9502494

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