Improving the performance of deep neural networks (DNNs) is important to both the compiler and neural architecture search (NAS) communities. Compilers apply program transformations in order to exploit hardware parallelism and memory hierarchy. However, legality concerns mean they fail to exploit the natural robustness of neural networks. In contrast, NAS techniques mutate networks by operations such as the grouping or bottlenecking of convolutions, exploiting the resilience of DNNs. In this work, we express such neural architecture operations as program transformations whose legality depends on a notion of representational capacity. This allows them to be combined with existing transformations into a unified optimization framework. This unification allows us to express existing NAS operations as combinations of simpler transformations. Crucially, it allows us to generate and explore new tensor convolutions. We prototyped the combined framework in TVM and were able to find optimizations across different DNNs, that significantly reduce inference time - over 3$\times$ in the majority of cases.
Furthermore, our scheme dramatically reduces NAS search time. Code is available at~\href{https://github.com/jack-willturner/nas-as-program-transformation-exploration}{this https url}.
Learning Syntactic Program Transformations from Examples
UFCG（坎皮纳联邦大学）；UC Berkeley（加州大学伯克利分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Washington（华盛顿大学）；Microsoft（微软）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy
CNIC, CAS（中国科学院计算机网络信息中心）；University of CAS（中国科学院大学）；University of Kansas（堪萨斯大学）；Great Bay University（大湾区大学）；IR A*STAR（新加坡科技研究局智能研究院）；Tsinghua University（清华大学）
FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）；School of Computing and Information Technology, Great Bay University（大湾区大学计算与信息技术学院）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Arizona State University（亚利桑那州立大学）
The Exploration of Neural Collapse under Imbalanced Data
Huazhong University of Science and Technology（华中科技大学）；School of Mathematics and Statistics（数学与统计学学院）；Institute of Interdisciplinary Research for Mathematics and Applied Science（数学与应用科学交叉研究院）
