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Spiral/AIML: Resource-Constrained Co-Optimization for High-Performance, Data-Intensive Computing

Watch SEI Principal Investigator, Dr. Scott McMillan, and research collaborator, CMU ECE Professor Franz Franchetti, discuss a community research effort to develop tools to reduce the prohibitive cost of implementing and re-implementing AI/ML software on

Software Engineering Institute



As the military adopts AI/ML to augment human teams, the cost of implementing and re-implementing AI/ML software on new hardware platforms will be prohibitive. To address these challenges, we propose to develop a hardware-software co-optimization system that will (1) automatically search and select hardware configurations optimized for a specified computation and (2)  autonomously generate optimized codes for the selected hardware configuration and the irregular, data-intensive computations required for AI/ML algorithms.