HSCoNAS : hardware-software co-design of efficient DNNs via neural architecture search

• Main Authors: Luo, Xiangzhong, Liu, Di, Huai, Shuo, Liu, Weichen
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Performance Evaluation; Runtime
• Online Access: https://hdl.handle.net/10356/155784
• Institution: Nanyang Technological University

In this paper, we present a novel multi-objective hardware-aware neural architecture search (NAS) framework, namely HSCoNAS, to automate the design of deep neural networks (DNNs) with high accuracy but low latency upon target hardware. To accomplish this goal, we first propose an effective hardware...