超低功耗时序边沿推断的TCN映射优化
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference
摘要：
时间卷积网络（TCN）是用于时间序列分析的新兴轻量级深度学习模型。我们介绍了一种自动化的探索方法和优化的内核库，以映射TCN的并行超低功耗（PULP）微控制器。我们的方法最大限度地减少延迟和能源，利用层平铺优化器，共同找到平铺尺寸和选择的因果关系和膨胀的一维卷积运算在TCN的核心的替代实现。我们在商业PULP设备上对我们的方法进行了基准测试，与在STM32 L4上执行的Cube-AI工具包相比，延迟降低了103倍，能耗降低了20.3倍，与相同硬件目标上的商业闭源和学术开源方法相比，能耗降低了2.9倍至26.6倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
