Optimizing CNN Model Inference on CPUs. - Yizhi Liu; Yao Wang; Ruofei Yu; Mu Li 003; Vin Sharma; Yida Wang 003 (USENIX ATC, 2019)

Efficient Execution of Quantized Deep Learning Models: A Compiler Approach. - Animesh Jain; Shoubhik Bhattacharya; Masahiro Masuda; Vin Sharma; Yida Wang 003 (CoRR, 2020)

Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference. - Haichen Shen; Jared Roesch; Zhi Chen 030; Wei Chen; Yong Wu; Mu Li 003; Vin Sharma; Zachary Tatlock; Yida Wang 003 (MLSys, 2021)

Tuna: A Static Analysis Approach to Optimizing Deep Neural Networks. - Yao Wang; Xingyu Zhou; Yanming Wang; Rui Li 033; Yong Wu; Vin Sharma (CoRR, 2021)

Bring Your Own Codegen to Deep Learning Compiler. - Zhi Chen 030; Cody Hao Yu; Trevor Morris; Jorn Tuyls; Yi-Hsiang Lai; Jared Roesch; Elliott Delaye; Vin Sharma; Yida Wang 003 (CoRR, 2021)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)