Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) method. 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, such as 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, such as 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 the conventional convolutional neural network (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.