Spiral/AIML: Co-optimization for High-Performance, Data-Intensive Computing in Resource Constrained Environments
October 2019 • Presentation
Scott McMillan, Franz Franchetti (Carnegie Mellon University)
Data-intensive computing is pervasive. This presentation provides an update on research to allow platform developers to realize high-performance AI/ML applications on leading-edge hardware architectures faster and cheaper.
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.