10:25 - 10:30 Welcome

10:30 - 11:15 "Shared Clusters for Machine Learning: Through the looking glass", by Prof. Shivaram Venkataraman, University of Wisconsin [Invited Talk]

11:15 - 11:30 Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust and Vijay Janapa Reddi, "QuaRL: Quantized Reinforcement Learning"

11:30 - 11:45 Trevor Gale, Matei Zaharia, Cliff Young and Erich Elsen, "Optimizing Sparse Matrix Operations for Deep Learning" (7)

11:45 - 12:00 Yancey Wang, Rong Ge and Shuang Qiu, "Energy-Aware DNN Graph Optimization"

12:00 - 14:00 Lunch

14:00 - 14:45 "Low-Precision Arithmetic in Machine Learning Systems", by Prof. Christopher De Sa, Cornell [Invited Talk]

14:45 - 15:00 Yury Pisarchyk and Juhyun Lee, "Efficient Memory Management for Deep Neural Net Inference"

15:00 - 15:15 Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang and Song Han, "Once for All: Train One Network and Specialize it for Efficient Deployment"

15:15 - 15:30 Kevin Kiningham, Philip Levis and Christopher Ré, "GReTA: Hardware Optimized Graph Processing For GNNs" (8)

15:30 - 16:00 Afternoon Break

16:00 - 16:15 Zhijing Li, Christopher De Sa and Adrian Sampson, "Optimizing JPEG Quantization for Classification Networks"

16:15 - 16:30 Sheng-Chun Kao, Arun Ramamurthy, Reed Williams and Tushar Krishna, "Conditional Neural Architecture Search"

16:30 - 16:45 Xiaomin Li, Cody Blakeney and Ziliang Zong, "Transfer Learning with Fine-grained Sparse Networks: From Efficient Network Perspective"

16:45 - 17:00 Closing remarks