Transfer-Once-For-All: AI Model Optimization for Edge

• URL: http://arxiv.org/abs/2303.15485v2
• Date: Sun, 2 Jul 2023 17:21:51 GMT
• Title: Transfer-Once-For-All: AI Model Optimization for Edge
• Authors: Achintya Kundu (IBM Research), Laura Wynter (IBM Research), Rhui Dih Lee (IBM Research), Luis Angel Bathen (IBM Research)
• Abstract summary: We propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost. To overcome the challenges arising from small data, TOFA utilizes a unified semi-supervised training loss to simultaneously train all existings within the supernet.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge deployment scenarios. Given a task, TOFA obtains custom neural networks, both the topology and the weights, optimized for any number of edge deployment scenarios. To overcome the challenges arising from small data, TOFA utilizes a unified semi-supervised training loss to simultaneously train all subnets within the supernet, coupled with on-the-fly architecture selection at deployment time.

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