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|Title:||HACScale : hardware-aware compound scaling for resource-efficient DNNs||Authors:||Kong, Hao
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2022||Source:||Kong, H., Liu, D., Luo, X., Liu, W. & Subramaniam, R. (2022). HACScale : hardware-aware compound scaling for resource-efficient DNNs. 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), 708-713. https://dx.doi.org/10.1109/ASP-DAC52403.2022.9712593||Project:||M4082282
|Abstract:||Model scaling is an effective way to improve the accuracy of deep neural networks (DNNs) by increasing the model capacity. However, existing approaches seldom consider the underlying hardware, causing inefficient utilization of hardware resources and consequently high inference latency. In this paper, we propose HACScale, a hardware-aware model scaling strategy to fully exploit hardware resources for higher accuracy. In HACScale, different dimensions of DNNs are jointly scaled with consideration of their contributions to hardware utilization and accuracy. To improve the efficiency of width scaling, we introduce importance-aware width scaling in HACScale, which computes the importance of each layer to the accuracy and scales each layer accordingly to optimize the trade-off between accuracy and model parameters. Experiments show that HACScale improves the hardware utilization by 1.92× on ImageNet, as a result, it achieves 2.41% accuracy improvement with a negligible latency increase of 0.6%. On CIFAR-10, HACScale improves the accuracy by 2.23% with only 6.5% latency growth.||URI:||https://hdl.handle.net/10356/155808||ISBN:||9781665421355||DOI:||10.1109/ASP-DAC52403.2022.9712593||Rights:||© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ASP-DAC52403.2022.9712593.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Conference Papers|
Updated on May 16, 2022
Updated on May 16, 2022
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