Hidden costs for inference with deep network on embedded system devices

Chankyu Lee; Woohyun Choi; Sangwook Park

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arXiv · 2601.01698

Hidden costs for inference with deep network on embedded system devices

Chankyu Lee·Woohyun Choi·Sangwook Park

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Abstract

This study evaluates the inference performance of various deep learning models under an embedded system environment. In previous works, Multiply-Accumulate operation is typically used to measure computational load of a deep model. According to this study, however, this metric has a limitation to estimate inference time on embedded devices. This paper poses the question of what aspects are overlooked when expressed in terms of Multiply-Accumulate operations. In experiments, an image classification task is performed on an embedded system device using the CIFAR-100 dataset to compare and analyze the inference times of ten deep models with the theoretically calculated Multiply-Accumulate operations for each model. The results highlight the importance of considering additional computations between tensors when optimizing deep learning models for real-time performing in embedded systems.

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BibTeX RIS

Chankyu Lee, Woohyun Choi, Sangwook Park. 2026-01-05. Hidden costs for inference with deep network on embedded system devices. https://arxiv.org/abs/2601.01698

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