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Reading List
Curated resources for Machine Learning Acceleration — research papers, books, videos, and more.
📄 Papers
Explainable-DSE: Agile and Explainable Exploration of DL Accelerator Codesigns
Shail Dave, Tony Nowatzki, Aviral Shrivastava
An agile DSE framework using bottleneck analysis for efficient hardware/software codesign of DNN accelerators.
Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer
Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks
Yu-Hsin Chen, Joel Emer, and Vivienne Sze
In-datacenter performance analysis of a tensor processing unit
Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, et al.
dMazeRunner: Executing Perfectly Nested Loops on Dataflow Accelerators
Shail Dave, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee, Aviral Shrivastava
Timeloop: A Systematic Approach to DNN Accelerator Evaluation
Angshuman Parashar, Priyanka Raina, Sophia Shao, et al.
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
Shail Dave, Riyadh Baghdadi, Tony Nowatzki, et al.
TVM: end-to-end optimization stack for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally