Co-exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks

摘要

Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and efficient. Recently, two-stage NAS, e.g. BigNAS, decouples the model training and searching process and achieves remarkable search efficiency and accuracy. Two-stage NAS requires sampling from the search space during training, which directly impacts the accuracy of the final searched models. While uniform sampling has been widely used for its simplicity, it is agnostic of the model performance Pareto front, which is the main focus in the search process, and thus, misses opportunities to further improve the model accuracy. In this work, we propose AttentiveNAS that focuses on improving the sampling strategy to achieve better performance Pareto. We also propose algorithms to efficiently and effectively identify the networks on the Pareto during training. Without extra re-training or post-processing, we can simultaneously obtain a large number of networks across a wide range of FLOPs. Our discovered model family, AttentiveNAS models, achieves top-1 accuracy from 77.3% to 80.7% on ImageNet, and outperforms SOTA models, including BigNAS and Once-for-All networks. We also achieve ImageNet accuracy of 80.1% with only 491 MFLOPs.

李萌

助理教授、研究员、博雅青年学者

李萌，北京大学人工智能研究院和集成电路双聘助理教授、研究员、博雅青年学者。他的研究兴趣集中于高效、安全的多模态人工智能加速算法和芯片，旨在通过算法到芯片的跨层次协同设计和优化，为人工智能构建高能效、高可靠、高安全的算力基础。