论文标题
受脑启发的高度计算：边缘计算的热友好型？
Brain-Inspired Hyperdimensional Computing: How Thermal-Friendly for Edge Computing?
论文作者
论文摘要
受脑启发的高维计算（HDC）是一种新兴的机器学习（ML）方法。它基于二进制或双极符号的大量向量以及一些简单的数学操作。 HDC的承诺是诸如可穿戴设备之类的嵌入式系统的高效实现。尽管已经提出了快速实现，但边缘计算尚未考虑其他约束。在这项工作中，我们旨在回答用于边缘计算的热友好的HDC。智能手表，智能眼镜甚至移动系统等设备由于量有限而具有限制性冷却预算。尽管HDC操作很简单，但向量很大，导致了大量的CPU操作，因此整个系统上的重负载可能会导致违规。在这项工作中，首次研究了HDC对芯片温度的影响。我们测量商业嵌入式系统的温度和功耗，并将HDC与常规CNN进行比较。我们透露，HDC的温度高达6.8°C高达6.8°C，并导致高达47％的CPU节流。即使HDC和CNN都针对相同的吞吐量（即执行相似数量的分类），由于较大的功耗，HDC仍会引起较高的芯片温度。
Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) methods. It is based on large vectors of binary or bipolar symbols and a few simple mathematical operations. The promise of HDC is a highly efficient implementation for embedded systems like wearables. While fast implementations have been presented, other constraints have not been considered for edge computing. In this work, we aim at answering how thermal-friendly HDC for edge computing is. Devices like smartwatches, smart glasses, or even mobile systems have a restrictive cooling budget due to their limited volume. Although HDC operations are simple, the vectors are large, resulting in a high number of CPU operations and thus a heavy load on the entire system potentially causing temperature violations. In this work, the impact of HDC on the chip's temperature is investigated for the first time. We measure the temperature and power consumption of a commercial embedded system and compare HDC with conventional CNN. We reveal that HDC causes up to 6.8°C higher temperatures and leads to up to 47% more CPU throttling. Even when both HDC and CNN aim for the same throughput (i.e., perform a similar number of classifications per second), HDC still causes higher on-chip temperatures due to the larger power consumption.
