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Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators


Jan 27, 2021
Hamzah Abdel-Aziz, Ali Shafiee, Jong Hoon Shin, Ardavan Pedram, Joseph H. Hassoun

* Accepted to appear in 4th Conference on Machine Learning and Systems 2021 

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Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators


Jan 13, 2020
Noah Gamboa, Kais Kudrolli, Anand Dhoot, Ardavan Pedram


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CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks


Jun 08, 2017
Yuanfang Li, Ardavan Pedram

* ASAP 2017: The 28th Annual IEEE International Conference on Application-specific Systems, Architectures and Processors 

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A Systematic Approach to Blocking Convolutional Neural Networks


Jun 14, 2016
Xuan Yang, Jing Pu, Blaine Burton Rister, Nikhil Bhagdikar, Stephen Richardson, Shahar Kvatinsky, Jonathan Ragan-Kelley, Ardavan Pedram, Mark Horowitz


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EIE: Efficient Inference Engine on Compressed Deep Neural Network


May 03, 2016
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally

* External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly: https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision: http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University: https://goo.gl/6lwuer. Published as a conference paper in ISCA 2016 

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