论文标题
TCN映射优化超低功率时序边缘推理
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference
论文作者
论文摘要
时间卷积网络（TCN）是新兴的轻量级深度学习模型，用于时间序列分析。我们介绍了一种自动探索方法和一个优化内核库，以在平行的超低功率（PULP）微控制器上绘制TCN。我们的方法通过利用图层瓷砖优化器来共同找到瓷砖尺寸，并在TCN核心的替代实现和扩张的1D-Convolution操作中选择。我们在商用纸浆设备上进行了基准测试，比在STM32L4上执行的立方体工具包的潜伏期低于103倍，与在同一硬件目标上的商业封闭源和学术开放式方法相比，能量低于2.9倍至26.6倍。
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.
