Accepted Papers
Apollo: Transferable Architecture Exploration. Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin, Yanqi Zhou, Albin Jones, Kevin Swersky, Milad Hashemi, Satrajit Chatterjee, Ravi Narayanaswami, and James Laudon.
A Deep Learning Based Cost Model for Automatic Code Optimization. Riyadhi Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Benatchba Karima, and Saman Amarasinghe.
A General Framework For VLSI Tool Parameter Optimization with Deep Reinforcement Learning. Anthony Agnesina, Sai Pentapati, Sung Kyu Lim.
CADET: A Systematic Method For Debugging Misconfigurations using Counterfactual Reasoning. Md Shahriar Iqbal*, Rahul Krishna, Mohammad Ali Javidian*, Baishakhi Ray, and Pooyan Jamshidi
ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures. Niranjan Hasabnis, Justin Gottschlich.
DEff-ARTS: Differentiable Efficient ARchiTecture Search. Sulaiman Sadiq, Partha Maji, Jonathan Hare, and Geoff Merrett.
FirePlace: Placing FireCracker Virtual Machines with Hindsight Imitation. Bharathan Balaji, Christopher Kakovitch, and Balakrishnan Narayanaswamy.
Highly Available Data Parallel ML training on Mesh Networks. Sameer Kumar, Norm Jouppi.
Learned Indexes for a Google-scale Disk-based Database. Hussam Abu-Libdeh, Deniz Altınbuüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou (Steve) Li, Andy Ly, and Christopher Olston.
The Law of Attraction: Affinity-Aware Placement Optimization using Graph Neural Networks . Yi-Chen Lu, Sai Pentapati, and Sung Kyu Lim.
Learning Local Advantage Functions for Generalizable Graph Optimizations. Yifan Wu, Yanqi Zhou, Phitchaya Mangpo Phothilimthana, Hanxiao Liu, Sudip Roy, and Azalia Mirhoseini.
Matrix Profile Index Prediction for Streaming Time Series. Maryam Shahcheraghi, Trevor Cappon, Samet Oymak, Evangelos Papalexakis, Eamonn Keogh, Zachary Zimmerman, and Philip Brisk.
NVCell: Generate Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning. Haoxing Ren, Matthew Fojtik, and Brucek Khailany.
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. Shauharda Khadka*, Estelle Aflalo*, Mattias Marder*, Avrech Ben-David*, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, and Somdeb Majumdar.
Program Graphs for Machine Learning. Chris Cummins*, Zacharias Fisches*, Tal Ben-Nun, Torsten Hoefler, Hugh Leather, and Michael O'Boyle.
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication. Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, and Johannes Gehrke.
Using Bayesian Optimization for Hardware/Software Co-Design of Neural Accelerators. Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, and Calvin Lin.
Value Function Based Performance Optimization of Deep Learning Workloads. Benoit Steiner, Chris Cummins, Horace He, and Hugh Leather.