  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.
