EMC2 - Energy Efficient Machine Learning and Cognitive Computing
memory Upcoming Workshops
- 10th Edition on March 02, 2025 in Las Vegas, NV, USA co-located with HPCA 2025
schedule Program for EMC2 at HPCA 2025
08:00 - 08:15
Welcome and Opening Remarks
Satyam Srivastava, d-Matrix
08:15 - 09:00
09:00 - 09:30
Craylm v0.5: Unifying LLM Inference and Training for RL Agents
Greg Diamos, Celestial AI
09:30 - 10:00
10:00 - 10:15
10:15 - 11:00
Efficient Large Language Models and Generative AI
Song Han, MIT
11:00 - 11:30
ML Workloads in AR/VR and Their Implications to the ML System Design
Hyoukjun Kwon, UC Irvine
11:30 - 12:00
12:00 - 13:00
13:00 - 13:45
Memory-Centric Computing: Enabling Fundamentally Efficient Computing Systems
Onur Mutlu, ETH Zurich
14:15 - 14:45
nanoML: Pushing the Limits of Edge AI with Weightless Neural Networks
Lizy Kurian John, UT Austin
14:45 - 15:00
15:00 - 16:00
16:00 - 16:30
Invited Talk
To Be Announced,
16:30 - 17:00
Invited Talk
To Be Announced,
17:00 - 17:30
17:30 - 18:00
Invited Talk
To Be Announced,
18:00 - 18:15
Closing Remarks
Sushant Kondguli, Meta
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.
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
Recent Editions
- 9th Edition co-located with ASPLOS 2024 in San Diego, CA, USA April 27, 2024
- 8th Edition co-located with AAAI 2023 in Washington DC, USA February 14, 2023
- 7th Edition in Virtual (from San Jose, California) August 28, 2021
- 6th Edition in Virtual (from San Jose, California) YouTube December 05, 2020
- 5th Edition co-located with NeurIPS 2019 in Vancouver BC, Canada [Proceedings: IEEE Xplore] December 13, 2019