The 10th EMC2 - Energy Efficient Machine Learning and Cognitive Computing
Co-located with the IEEE International Symposium on High-Performance Computer Architecture HPCA 2025
description Workshop Objective
With the advent of ChatGPT and other language models, Generative AI and LLMs have captured the imagination of whole world! A new wave of intelligent computing, driven by recent advances in machine learning and cognitive algorithms coupled with processtechnology and new design methodologies, has the potential to usher unprecedented disruption in the way modern computing systemsare designed and deployed. These new and innovative approaches often provide an attractive and efficient alternative not only in terms of performance but also power, energy, and area. This disruption is easily visible across the whole spectrum of computing systems– ranging from low end mobile devices to large scale data centers. Applications that benefit from efficient machine learning include computer vision and image processing, augmented/mixed reality, language understanding, speech and gesture recognition, malware detection, autonomous driving, and many more. Naturally, these applications have diverse requirements for performance, energy, reliability, accuracy, and security that demand a holistic approach to designing the hardware, software, and intelligence algorithms to achieve the best outcome.
chat Call for Papers
The goal of this Workshop is to provide a forum for researchers and industry experts who are exploring novel ideas, tools and techniques to improve the energy efficiency of MLLMs as it is practised today and would evolve in the next decade. We envision that only through close collaboration between industry and the academia we will be able to address the difficult challenges and opportunities of reducing the carbon footprint of AI and its uses. We have tailored our program to best serve the participants in a fully digital setting. Our forum facilitates active exchange of ideas through:
- Keynotes, invited talks and discussion panels by leading researchers from industry and academia
- Peer-reviewed papers on latest solutions including works-in-progress to seek directed feedback from experts
- Independent publication of proceedings through IEEE CPS
We invite full-length papers describing original, cutting-edge, and even work-in-progress research projects about efficient machine learning. Suggested topics for papers include, but are not limited to the ones listed on this page. The proceedings from previous instances have been published through the prestigious IEEE Conference Publishing Services (CPS) and are available to the community via IEEE Xplore. In each instance, IEEE conducted independent assessment of the papers for quality.
format_list_bulleted Topics for the Workshop
- Neural network architectures for resource constrained applications
- Efficient hardware designs to implement neural networks including sparsity, locality, and systolic designs
- Power and performance efficient memory architectures suited for neural networks
- Network reduction techniques – approximation, quantization, reduced precision, pruning, distillation, and reconfiguration
- Exploring interplay of precision, performance, power, and energy through benchmarks, workloads, and characterization
- Simulation and emulation techniques, frameworks, tools, and platforms for machine learning
- Optimizations to improve performance of training techniques including on-device and large-scale learning
- Load balancing and efficient task distribution, communication and computation overlapping for optimal performance
- Verification, validation, determinism, robustness, bias, safety, and privacy challenges in AI systems