Call for Research Papers

Abstract deadline: July 01 July 10, 2024, 11:59 pm AoE.

Paper submissions due: July 15 July 22, 2024, 11:59 pm AoE.

AI-ML Systems 2024 is the 4th edition of the AI-ML Systems conference targeting research at the intersection of Artificial Intelligence (AI) and Machine Learning (ML) techniques and systems engineering. Through this conference we plan to bring out and highlight the natural connections between these two fields and their application to socio-economic systems. Specifically we explore how immense strides in AI/ML techniques are made possible through computational systems research (e.g., improvements in CPU/GPU architectures, data-intensive infrastructure, and communications ), how the use of AI/ML can help in the continuous and workload-driven design space exploration of computational systems (e.g., self-tuning databases, learning compiler optimisers, and learnable network systems ), and the use of AI/ML in the design of socio-economic systems such as public healthcare, and security. The goal is to bring together these diverse communities and elicit connections between them.

The use of artificial intelligence (AI)–generated text in an article shall be disclosed in the acknowledgments section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text

  • Abstract deadline: July 10, 2024, 11:59 pm AoE.
  • Paper submissions due: July 22, 2024, 11:59 pm AoE.
  • Author notifications: Aug 15, 2024, 11:59 pm AoE.
  • Camera ready deadline: Aug 31, 2024, 11:59 pm AoE.
  • Conference dates: Oct 08-11, 2024

Topics of Interest

The areas of interest are broadly categorized into the following three streams: (including but not limited to)

  • CPU/GPU architectures for AI/ML
  • Specialized/Embedded hardware for AI/ML workloads
  • Data intensive systems for efficient and distributed training
  • Challenges in production deployment of AI/ML systems
  • AI/ML programming models, languages, and abstractions
  • AI/ML compilers and runtime
  • Efficient systems for data preparation and processing
  • Systems for visualization of data, models, and predictions
  • Testing, debugging, and monitoring of AI/ML applications
  • Cloud-computing for machine and deep learning
  • Machine and deep learning “as-a-service”
  • Tiny Machine Learning
  • Embedded and Edge AI
  • Pervasive AI
  • Federated, distributed and parallel learning algorithms
  • MLOps (data collection, monitoring and re-training)
  • Efficient inference for deep learning models
  • Logging mechanisms for deep learning models
  • AI/ML for VLSI and architecture design
  • AI/ML in compiler optimization
  • AI/ML in data management – including database optimizations, virtualization, etc.
  • AI/ML for networks – design of networks, load modeling, etc.
  • AI/ML for power management – green computing, power models, etc.
  • AI/ML for Cloud Computing
  • AI/ML for IOT networks
  • AI/ML for HPC
  • Deep Learning Architecture and applications
  • Computer Vision and Image processing
  • Natural language processing and understanding
  • Speech signal processing and Socio-Economic Systems
  • AI/ML in cyber-physical systems
  • Privacy, Security, and Robustness in AI/ML systems
  • Ethics in AI/ML systems
  • Fairness, Transparency, Interpretability and Explainability in AI/ML 
  • Sustainable AI/ML
  • AI for Healthcare
  • AI for Resource Allocation, Econometrics, and Finance
  • AI for Energy
  • AI for Transportation and Built Environment
  • AI for Climate Change & Sustainability
  • AI for Education
  • AI for Art, Music, and Sound
Style and Author Instructions
  • Research papers must not exceed 8 pages, excluding appendix, acknowledgment and bibliography. Only electronic submissions in PDF format using the ACM sigconf template (see will be considered for authors using Latex. For authors writing in Microsoft Word, MS Word submissions should use the official Interim Template provided by ACM here. Submissions will be handled through can be submitted under any of the three main topics listed above. Authors are required to make a primary topic selection, with optional secondary topics for each paper. Number of papers accepted under each topic is not capped. We will accept all papers that meet the high quality and innovation levels required by the AI-ML Systems conference. All papers that are accepted will appear in the proceedings.

    All accepted papers will be presented as posters at AI-ML Systems 2024, but a selected subset of them will be given a “conventional” (oral) presentation slot during the conference. However, all accepted papers will be treated equally in the conference proceedings, which are the persistent, archival record of the conference.

  • Plagiarism Policy: Submission of papers to AIMLSystems 2024 carries with it the implied agreement that the paper represents original work. We will follow the ACM Policy on Plagiarism, Misrepresentation, and Falsification – see All submitted papers will be subjected to a “similarity test”. Papers achieving a high similarity score will be examined and those that are deemed unacceptable will be rejected without a formal review. We also expect to report such unacceptable submissions to the superiors of each of the authors.

    Submission of papers to AI-ML Systems 2024 also carries with it the implied agreement that one or more of the listed authors will register for and attend the conference and present the paper. Papers not presented at the conference will not be included in the final program or in the digital proceedings. Therefore, authors are strongly encouraged to plan accordingly before deciding to submit a paper.

  • During submission of a research paper, the submission site will request information about Conflicts of Interest (COI) of the paper’s authors with program committee (PC) members. It is the full responsibility of all authors of a paper to identify all (and only) PC members with potential COIs as per the definition provided on the submission site. Papers with incorrect or incomplete COI information as of the submission closing time are subject to immediate rejection.

    Definition of Conflict of Interest (COI): A paper author has a COI with a PC member when and only when one or more of the following conditions hold:

    • The PC member is a co-author of the paper, or has been a co-author of a paper in the last 3 years or 4 (or more) papers in the last 10 years.
    • The PC member has been a co-worker in the same company or university within the past two years.
    • The PC member has been a collaborator within the past two years.
    • The PC member is or was the author’s primary thesis advisor, no matter how long ago.
    • The author is or was the PC member’s primary thesis advisor, no matter how long ago.
    • The PC member is a relative or close personal friend of the author.
  • Paper Format: Please prepare your submission using a double-column format. This format allows for optimal readability and consistency throughout the proceedings.
  • Double-Blind Review: In order to maintain anonymity during the review process, please refrain from including author names and affiliations in the paper. This will help ensure an unbiased evaluation of your work.
  • Paper Upload: Kindly upload the PDF file of your paper. The maximum file size allowed for submission is 20MB.
  • A paper submitted to AI-ML Systems can not be under review at any other conference or journal during the entire time it is considered for review at AI-ML Systems, and it must be substantially different from any previously published work or any work under review. After submission and during the review period, submissions to AI-ML Systems must not be submitted to other conferences / journals for consideration. However, authors may publish at non-archival venues, such as workshops without proceedings, or as technical reports (including arXiv).

The conference is planned to be an in-person event. At least one author of each accepted paper is expected to attend and present at the conference in person.