HACScale : hardware-aware compound scaling for resource-efficient DNNs

• Main Authors: Kong, Hao, Liu, Di, Luo, Xiangzhong, Liu, Weichen, Subramaniam, Ravi
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Deep Learning; Design Automation
• Online Access: https://hdl.handle.net/10356/155808
• Institution: Nanyang Technological University

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 pape...