Brain-Inspired Hyperdimensional Computing: How Thermal-Friendly for Edge Computing?

TL;DR

This paper investigates the thermal impact of hyperdimensional computing (HDC) on embedded systems, revealing that HDC causes higher temperatures and more CPU throttling compared to CNNs, raising concerns for edge device deployment.

Contribution

It provides the first measurement-based analysis of HDC's thermal effects on embedded hardware, comparing it with CNNs under realistic conditions.

Findings

HDC causes up to 6.8°C higher temperatures than CNN.

HDC leads to up to 47% more CPU throttling.

HDC consumes more power than CNN for similar throughput.

Abstract

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{\deg}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.