What to expect

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.

The workshop will host 6 speakers and we invite researchers to submit relevant papers through our call for papers.

• ¹ The Case for Learned Index Structures
• ² Learning to Design Circuits
• ³ A graph placement methodology for fast chip design
• ⁴ DRiLLS: Deep Reinforcement Learning for Logic Synthesis
• ⁵ Park: An Open Platform for Learning-Augmented Computer Systems
• ⁶ Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
• ⁷ Benchmarking learned indexes
• ⁸ Learning to Optimize Join Queries With Deep Reinforcement Learning
• ⁹ Learning memory access patterns
• ¹⁰ Learning-based Memory Allocation for C++ Server Workloads
• ¹¹ AI and Compute
• ¹² Machine learning in compiler optimization
• ¹³ Learning to Optimize Tensor Programs
• ¹⁴ Learning scheduling algorithms for data processing clusters
• ¹⁵ ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
• ¹⁶ Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks
• ¹⁷ A Deep Learning Based Cost Model for Automatic Code Optimization
• ¹⁸ The Law of Attraction: Affinity-Aware Placement Optimization using Graph Neural Networks