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Artificial Intelligence and Machine Learning
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2019

MAGNet: A Modular Accelerator Generator for Neural Networks

Rangharajan Venkatesan, Sophia Shao, Miaorong Wang, Jason Clemons, Steve Dai, Matt Fojtik, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Yanqing Zhang, Brian Zimmer, William Dally, Joel Emer, Steve Keckler, Brucek Khailany

Understanding Reuse, Performance, and Hardware Cost of DNN Dataflows: A Data-Centric Approach.

Hyoukjun Kwon, Prasanth Chatarasi, Michael Pellauer, Angshuman Parashar, Vivek Sarkar, Tushar Krishna

IEEE Micro Top Picks in Computer Architecture

Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture

Sophia Shao, Jason Clemons, Rangharajan Venkatesan, Brian Zimmer, Matt Fojtik, Ted Jiang, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Stephen Tell, Yanqing Zhang, William Dally, Joel Emer, Tom Gray, Brucek Khailany, Steve Keckler

Best Paper award
IEEE Micro Top Picks in Computer Architecture (Honorable Mention)

Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs

Esha Choukse, Michael B. Sullivan, Mike O'Connor, Mattan Erez, Jeff Pool, David Nellans, Stephen W. Keckler

DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis

Sankug Lym, Donghyuk Lee, Niladrish Chatterjee, Mike O'Connor, Mattan Erez

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