Characterizing Hardware Utilization on Edge Devices when Inferring Compressed Deep Learning Models

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Implementing edge AI involves running AI algorithms near the sensors. Deep Learning (DL) Model has successfully tackled image classification tasks with remarkable performance. However, their requirements for huge computing resources hinder the implementation of edge devices. Compressing the model is an essential task to allow the implementation of the DL model on edge devices. Post-training quantization (PTQ) is a compression technique that reduces the bit representation of the model weight parameters. This study looks at the impact of memory allocation on the latency of compressed DL models on Raspberry Pi 4 Model B (RPi4B) and NVIDIA Jetson Nano (J. Nano). This research aims to understand hardware utilization in central processing units (CPU), graphics processing units (GPU),and memory. This study focused on the quantitative method, which controls memory allocation and measures warm-up time, latency, CPU, and GPU utilization. Speed comparison among inference of DL models on RPi4B and J. Nano. This paper observes the correlation between hardware utilization versus the various DL inference latencies. According to our experiment, we concluded that smaller memory allocation led to high latency on both RPi4B and J. Nano. CPU utilization on RPi4B. CPU utilization in RPi4B increases along with the memory allocation; however, the opposite is shown on J. Nano since the GPU carries out the main computation on the device. Regarding computation, thesmaller DL Size and smaller bit representation lead to faster inference (low latency), while bigger bit representation on the same DL model leads to higher latency.