What To Expect

The ML for Systems workshop presents cutting-edge work on ML in computer systems and aims to develop a unified methodology for the field.

Machine Learning (ML) for Systems describes the application of machine learning techniques to problems related to computer systems. By leveraging supervised learning and reinforcement learning (RL) approaches, machine learning can replace longstanding heuristics that currently drive many of these systems. This includes a wide range of topics, including multi-objective tasks such as designing new data structures ¹, integrated circuits ^{2, 3}, or design verification ^{20, 21}, as well as implementing control algorithms for applications such as compilers ^{12, 13, 19}, databases ⁸, memory management ^{9, 10}, or ML frameworks ¹¹. While the systems community increasingly recognizes the importance of ML in solving a variety of different systems problems ²³, ML for Systems remains an emerging area without widely established best practices, methods and strategies for the application of state-of-the-art machine learning techniques ²². The goal of this workshop is to provide an interdisciplinary venue for ML and Systems experts to push this boundary and start new directions within the ML for Systems area.

Workshop Direction

In previous 6 editions, we showcased specific approaches and frameworks to solve problems, bringing together researchers and practitioners at NeurIPS from both the 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 ^{11, 13, 14, 15, 16, 17, 18}. This year, we plan to continue this path while encouraging work in key emerging areas such as Large Language Model (LLM) training and serving, and unifying benchmarks on key problems such as scheduling and compiling through a competition.

Recently, the rise of Large Language Models (LLMs) has presented new opportunities and challenges within the domain of computer systems. Our community is well-positioned to produce science and stimulate discussion for adapting to the new paradigm, especially how LLMs can be used to solve systems problems, and using ML to address systems issues that emerge from LLM training and serving. Additionally, as the field matures, we emphasize on keeping the research open, and the science reproducible. To that end, we are supplementing our main program with a competition track to crystallize the field’s progress.

Workshop Goals

NeurIPS provides a unique opportunity to bring together systems researchers and researchers from other sub-areas of ML who had not previously considered applying their techniques in a computer systems context. We see the goal of our workshop as solving the following two objectives:

• Opening up connections between research areas that were not previously considered, connecting the ML and Systems communities, growing the scope of ML for Systems work and unlocking new research opportunities.
• Developing best practices, methodologies and benchmarks for the ML for Systems field.

To build commonalities on the topic of LLMs interacting with computational systems, we specifically include seminal talks on emerging trends on training and serving LLMs from seasoned researchers and practitioners as a part of our invited speakers. Our call for papers also includes topics at the intersection of Systems and LLMs.

Our program include contributed speakers and poster sessions from selected works. Our schedule is available. We invited researchers to submit relevant papers through our call for papers.