EMC2 - Energy Efficient Machine Learning and Cognitive Computing

• 10th Edition on March 01, 2025 in Las Vegas, NV, USA co-located with 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 process technology and new design methodologies, has the potential to usher unprecedented disruption in the way modern computing systems are 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, 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. We invite full-length papers describing original, cutting-edge, and even work-in-progress research projects about efficient machine learning.

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