Hardware and Architecture

1. Hardware and Architecture Co-Leads: Ken Alvin (SNL), Travis Humble (ORNL), Katie Schuman (ORNL) Science Writer: Elizabeth Rosenthal + 25 Participants (available in spreadsheet)

2. AI Application Requirements for Hardware • System of systems architectures from edge computing to data centers • Complex data-driven workflows • Energy and environmental constraints on computing, particularly at the edge • AI systems must directly integrate with sensors and control elements and survive in their operational environments • Data reduction and data movement is key to most workflows • Use of data and AI for control of systems and experiments • Energy and performance requirements drive custom, heterogeneous, and novel computational devices • Domains have specific/unique workflow requirements, driving the need for co-design tools to architect systems • AI use cases within existing HPC and data centers

3. Research Opportunities • Capturing and characterization of workflow requirements as exemplar workflows, datasets, etc. • Adaptive and fast-changing • Co-design tools, benchmarks, and metrics • Design of distributed, adaptable system of systems architectures • Real-time and quality of service • Development, implementation, and integration of new edge computing devices with flexibility • Power constraints, radiation tolerant, data reduction, etc. • Data movement and access • Bandwidth, latency, reliability, etc. • Data center integration of specialized hardware • Design for new workflows • Scalability

4. Enabling Technologies for Hardware Research • Co-design tools • Wide area network or system of systems simulations • Architectural simulators • Proxy workflows • Neural architecture search (meta-learning) • Specialized hardware for AI • Examples: Neuromorphic, quantum, AI accelerators, photonics, FPGAs, ASICs, etc. • Testbeds and hardware evaluation labs • In the lab hardware integrated with example sensors from edge environments • Integration of heterogeneous components