Workshop
Machine Learning for Systems
Neel Kant · Martin Maas · Azade Nova · Benoit Steiner · Xinlei XU · Dan Zhang
Room 396
Sat 3 Dec, 6:30 a.m. PST
Machine Learning (ML) for Systems is an important direction for applying ML in the real world. It has been shown that ML can replace long standing heuristics in computer systems by leveraging supervised learning and reinforcement learning (RL) approaches. The computer systems community recognizes the importance of ML in tackling strenuous multi-objective tasks such as designing new data structures 1, integrated circuits 2,3, or schedulers, as well as implementing control algorithms for applications such as compilers 12,13, databases 8, memory management 9,10 or ML frameworks 6.
General Workshop Direction. This is the fifth iteration of this workshop. In previous editions, we showcased approaches and frameworks to solve problems, bringing together researchers and practitioners at NeurIPS from both ML and systems communities. While breaking new grounds, we encouraged collaborations and development in a broad range of ML for Systems works, many later published in top-tier conferences 6,13,14,15,16,17,18. This year, we plan to continue on this path while expanding our call for paper to encourage emerging works on minimizing energy footprint, reaching carbon neutrality, and using machine learning for system security and privacy.
Focusing the Workshop on Unifying Works. As the field of ML for Systems is maturing, we are adapting the focus and format of the workshop to evolve with it. The community has seen several efforts to consolidate different subfields of ML for Systems 4, 5, 6, 7. However, such efforts need more support. To boost recent advances in shared methodology, tools, and frameworks, this year we will welcome submissions presenting datasets, simulators, or benchmarks that can facilitate research in the area.
Schedule
Sat 6:30 a.m. - 6:40 a.m.
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Opening Remarks
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Opening Remarks
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SlidesLive Video |
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Sat 6:40 a.m. - 7:30 a.m.
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Jeff Dean - Invited Talk
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Talk
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Jeff Dean 🔗 |
Sat 7:30 a.m. - 8:15 a.m.
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Dawn Song - Invited Talk
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Talk
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SlidesLive Video |
Dawn Song 🔗 |
Sat 8:20 a.m. - 8:50 a.m.
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Poster Session | Coffee Break
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Sat 8:55 a.m. - 9:05 a.m.
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A code superoptimizer through neural Monte-Carlo tree search
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Spotlight
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SlidesLive Video |
Wenda Zhou · Olga Solodova · Ryan Adams 🔗 |
Sat 9:10 a.m. - 9:20 a.m.
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Predicting Network Buffer Capacity for BBR Fairness ( Spotlight ) > link | Ibrahim Umit Akgun · Santiago Vargas · Andrew Burford · Michael McNeill · Michael Arkhangelskiy · Aruna Balasubramanian · Anshul Gandhi · Erez Zadok 🔗 |
Sat 9:25 a.m. - 9:35 a.m.
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HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression ( Spotlight ) > link | Jiaqi Gu · Ben Keller · Jean Kossaifi · Anima Anandkumar · Brucek Khailany · David Pan 🔗 |
Sat 9:45 a.m. - 9:55 a.m.
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Learning to Drive Software-Defined Storage
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Spotlight
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SlidesLive Video |
Jian Huang · Daixuan Li · Jinghan Sun 🔗 |
Sat 11:05 a.m. - 11:20 a.m.
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Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration
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Spotlight
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SlidesLive Video |
Srivatsan Krishnan · Natasha Jaques · Shayegan Omidshafiei · Dan Zhang · Izzeddin Gur · Vijay Janapa Reddi · Aleksandra Faust 🔗 |
Sat 11:30 a.m. - 11:45 a.m.
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Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
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Spotlight
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SlidesLive Video |
Benjamin Fuhrer · Yuval Shpigelman · Chen Tessler · Shie Mannor · Gal Chechik · Eitan Zahavi · Gal Dalal 🔗 |
Sat 11:45 a.m. - 12:15 p.m.
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Poster Session | Coffee Break
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Sat 12:15 p.m. - 1:00 p.m.
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Steve Keckler - Invited Talk: Applying ML to Practical System Design
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Talk
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SlidesLive Video |
Stephen Keckler 🔗 |
Sat 1:05 p.m. - 1:50 p.m.
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Newsha Ardalani - Invited Talk
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Talk
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SlidesLive Video |
Newsha Ardalani 🔗 |
Sat 1:55 p.m. - 2:30 p.m.
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Riyadh Baghdadi - Invited Talk
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Talk
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SlidesLive Video |
Riyadh Baghdadi 🔗 |
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Towards Continually Learning Application Performance Models
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Poster - Recorded Presentation
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SlidesLive Video |
Ray Sinurat · Sandeep Madireddy · Anurag Daram · Haryadi Gunawi · Robert Ross 🔗 |
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Learning to Drive Software-Defined Storage ( Poster ) > link | Jian Huang · Daixuan Li · Jinghan Sun 🔗 |
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Predicting Network Buffer Capacity for BBR Fairness ( Poster ) > link | Ibrahim Umit Akgun · Santiago Vargas · Andrew Burford · Michael McNeill · Michael Arkhangelskiy · Aruna Balasubramanian · Anshul Gandhi · Erez Zadok 🔗 |
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LoopStack: ML-friendly ML Compiler Stack ( Poster - Recorded Presentation ) > link | Bram Wasti · Dejan Grubisic · Benoit Steiner · Aleksandar Zlateski 🔗 |
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Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR ( Poster - Recorded Presentation ) > link | Sami Alabed · Dominik Grewe · Juliana Franco · Bart Chrzaszcz · Tom Natan · Tamara Norman · Norman Rink · Dimitrios Vytiniotis · Michael Schaarschmidt 🔗 |
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Multi-objective Reinforcement Learning with Adaptive Pareto Reset for Prefix Adder Design ( Poster - Recorded Presentation ) > link | Jialin Song · Rajarshi Roy · Jonathan Raiman · Robert Kirby · Neel Kant · Saad Godil · Bryan Catanzaro 🔗 |
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Preference-Aware Constrained Multi-Objective Bayesian Optimization For Analog Circuit Design ( Poster ) > link | Alaleh Ahmadianshalchi · Syrine Belakaria · Jana Doppa 🔗 |
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The Case for Learning Machine Language
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Poster - Recorded Presentation
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SlidesLive Video |
Guangda Liu · Chieh-Jan Mike Liang · Shijie Cao · Shuai Lu · Leendert van Doorn 🔗 |
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HloEnv: A Graph Rewrite Environment for Deep Learning Compiler Optimization Research ( Poster - Recorded Presentation ) > link | Chin Yang Oh · Kunhao Zheng · Bingyi Kang · Xinyi Wan · Zhongwen Xu · Shuicheng Yan · Min Lin · Yangzihao Wang 🔗 |
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Robust Scheduling with GFlowNets
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Poster - Recorded Presentation
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SlidesLive Video |
David Zhang · Corrado Rainone · Markus Peschl · Roberto Bondesan 🔗 |
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A code superoptimizer through neural Monte-Carlo tree search
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Poster - Recorded Presentation
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link
SlidesLive Video |
Wenda Zhou · Olga Solodova · Ryan Adams 🔗 |
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Target-independent XLA optimization using Reinforcement Learning
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Poster - Recorded Presentation
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SlidesLive Video |
Milan Ganai · Haichen Li · Theodore Enns · Yida Wang · Randy Huang 🔗 |
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Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration ( Poster ) > link | Srivatsan Krishnan · Natasha Jaques · Shayegan Omidshafiei · Dan Zhang · Izzeddin Gur · Vijay Janapa Reddi · Aleksandra Faust 🔗 |
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An Efficient One-Class SVM for Novelty Detection in IoT
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Poster - Recorded Presentation
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SlidesLive Video |
Kun Yang · Samory Kpotufe · Nicholas Feamster 🔗 |
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Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs ( Poster ) > link | Benjamin Fuhrer · Yuval Shpigelman · Chen Tessler · Shie Mannor · Gal Chechik · Eitan Zahavi · Gal Dalal 🔗 |
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NeuralFuse: Improving the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
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Poster - Recorded Presentation
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SlidesLive Video |
Hao-Lun Sun · Lei Hsiung · Nandhini Chandramoorthy · Pin-Yu Chen · Tsung-Yi Ho 🔗 |
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Lattice Quantization
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Poster - Recorded Presentation
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link
SlidesLive Video |
ClĂ©ment Metz · Thibault Allenet · Johannes Thiele · Antoine DUPRET · Olivier BICHLER 🔗 |
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An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design ( Poster - Recorded Presentation ) > link | Mingjie Liu · Haoyu Yang · David Pan · Brucek Khailany · Mark Ren 🔗 |
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HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression ( Poster ) > link | Jiaqi Gu · Ben Keller · Jean Kossaifi · Anima Anandkumar · Brucek Khailany · David Pan 🔗 |