Workshop on Energy Efficient Machine Learning and Cognitive Computing

description Workshop Objective

As artificial intelligence and other forms of cognitive computing continue to proliferate into new domains, many forums for dialogue and knowledge sharing have emerged. In the proposed workshop, the primary focus is on the exploration of energy efficient techniques and architectures for cognitive computing and machine learning, particularly for applications and systems running at the edge. For such resource constrained environments, performance alone is never sufficient, requiring system designers to carefully balance performance with power, energy, and area (overall PPA metric).

The goal of this workshop is to provide a forum for researchers who are exploring novel ideas in the field of energy efficient machine learning and artificial intelligence for a variety of applications. We also hope to provide a solid platform for forging relationships and exchange of ideas between the industry and the academic world through discussions and active collaborations.

format_list_bulleted Topics for the Workshop

• Architectures for the edge: IoT, automotive, and mobile
• Approximation, quantization reduced precision computing
• Hardware/software techniques for sparsity
• Neural network architectures for resource constrained devices
• Neural network pruning, tuning and and automatic architecture search
• Novel memory architectures for machine learing
• Communication/computation scheduling for better performance and energy
• Load balancing and efficient task distribution techniques
• Exploring the interplay between precision, performance, power and energy
• Exploration of new and efficient applications for machine learning
• Characterization of machine learning benchmarks and workloads
• Performance profiling and synthesis of workloads
• Simulation and emulation techniques, frameworks and platforms for machine learning
• Power, performance and area (PPA) based comparison of neural networks
• Verification, validation and determinism in neural networks
• Efficient on-device learning techniques
• Security, safety and privacy challenges and building secure AI systems