Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

arXiv:2607.06922v1 Announce Type: new Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize performance is not intuitive. Particularly, many application scenarios are real-time and have a strict latency constraint, while conventional neural network optimization methods do not directly change the temporal cost of model inference for latency-critical edge systems. In this work, we propose a latency-oriented neural ne

Source: arXiv cs.LG — read the full report at the original publisher.

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