Spiral AI/ML Collection
• Collection
Publisher
Software Engineering Institute
Abstract
Implementing and re-implementing AI/ML software on new hardware platforms is expensive and time consuming. The SEI is developing a hardware/software co-optimization system that automatically picks the most suitable hardware configurations and generates optimized code for the selected hardware and AI/ML algorithms.
Collection Items
Spiral/AIML: Co-optimization for High-Performance, Data-Intensive Computing in Resource Constrained Environments
• Presentation
By 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.
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Spiral/AIML: Frontiers of Graph Processing in Linear Algebra
• Poster
By Scott McMillan, Franz Franchetti (Carnegie Mellon University)
This poster describes research to use a linear algebraic approach to graph algorithms
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Spiral/AIML: Resource-Constrained Co-Optimization for High-Performance, Data-Intensive Computing
• Video
By Scott McMillan, Franz Franchetti (Carnegie Mellon University)
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 …
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