Publications | Research

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Artificial Intelligence and Machine Learning
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2019

Timeloop: A Systematic Approach to DNN Accelerator Evaluation

Angshuman Parashar, Priyanka Raina, Yakun Sophia Shao, Yu-Hsin Chen, Victor A. Ying, Anurag Mukkara, Rangharajan Venkatesan, Brucek Khailany, Steve Keckler, Joel Emer

2018

UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition

Kartik Hegde, Jiyong Yu, Rohit Agrawal, Mengjia Yan, Michael Pellauer, Christopher W. Fletcher

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Youngeun Kwon, Steve Keckler

Stitch-X: An Accelerator Architecture for Exploiting Unstructured Sparsity in Deep Neural Networks

Ching-En Lee, Yakun Sophia Shao, Jie-Fang Zhang, Angshuman Parashar, Joel Emer, Steve Keckler, Zhengya Zhang

2017

Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications

Guanpeng Li, Siva Hari, Michael B. Sullivan, Timothy Tsai, Karthik Pattabiraman, Joel Emer, Steve Keckler

SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks

Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Steve Keckler, William Dally

2016

vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design.

Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Steve Keckler

Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

Yu-Hsin Chen, Joel Emer, Vivienne Sze

vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design

Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Steve Keckler