Project Grant 2528767

CAREER: EFFICIENT, DYNAMIC, ROBUST, AND ON-DEVICE CONTINUAL DEEP LEARNING WITH NON-VOLATILE MEMORY BASED IN-MEMORY COMPUTING SYSTEM -THIS AWARD IS FUNDED IN WHOLE OR IN PART UNDER THE AMERICAN RESCUE PLAN ACT OF 2021 (PUBLIC LAW 117-2). OVER PAST DECADES, THERE HAVE EXISTED GRAND CHALLENGES IN DEVELOPING HIGH PERFORMANCE AND ENERGY-EFFICIENT COMPUTING SOLUTIONS FOR BIG-DATA PROCESSING. MEANWHILE, OWING TO THE BOOM IN ARTIFICIAL INTELLIGENCE (AI), ESPECIALLY DEEP NEURAL NETWORKS (DNNS), SUCH BIG-DATA PROCESSING REQUIRES EFFICIENT, INTELLIGENT, FAST, DYNAMIC, ROBUST, AND ON-DEVICE ADAPTIVE COGNITIVE COMPUTING. HOWEVER, THOSE REQUIREMENTS ARE NOT SUFFICIENTLY SATISFIED BY EXISTING COMPUTING SOLUTIONS DUE TO THE WELL-KNOWN POWER WALL IN SILICON-BASED SEMICONDUCTOR DEVICES, THE MEMORY WALL IN TRADITIONAL VON-NEUMAN COMPUTING ARCHITECTURES, AND COMPUTATION-/MEMORY-INTENSIVE DNN COMPUTING ALGORITHMS. THIS PROJECT AIMS TO FOSTER A SYSTEMATIC BREAKTHROUGH IN DEVELOPING AI-IN-MEMORY COMPUTING SYSTEMS, THROUGH COLLABORATIVELY DEVELOPING AHYBRID IN-MEMORY COMPUTING (IMC) HARDWARE PLATFORM INTEGRATING THE BENEFITS OF EMERGING NON-VOLATILE RESISTIVE MEMORY (RRAM) AND STATIC RANDOM ACCESS MEMORY (SRAM) TECHNOLOGIES, AS WELL AS INCORPORATING IMC-AWARE DEEP-LEARNING ALGORITHM INNOVATIONS. THE OVERARCHING GOAL OF THIS PROJECT IS TO DESIGN, IMPLEMENT, AND EXPERIMENTALLY VALIDATE A NEW HYBRID IN-MEMORY COMPUTING SYSTEM THAT IS COLLABORATIVELY OPTIMIZED FOR ENERGY EFFICIENCY, INFERENCE ACCURACY, SPATIOTEMPORAL DYNAMICS, ROBUSTNESS, AND ON-DEVICE LEARNING, WHICH WILL GREATLY ADVANCE AI-BASED BIG-DATA PROCESSING FIELDS SUCH AS COMPUTER VISION, AUTONOMOUS DRIVING, ROBOTICS, ETC. THE RESEARCH WILL ALSO BE EXTENDED INTO AN EDUCATIONAL PLATFORM, PROVIDING A USER-FRIENDLY LEARNING FRAMEWORK, AND WILL SERVE THE EDUCATIONAL OBJECTIVES FOR K-12 STUDENTS, UNDERGRADUATE, GRADUATE, AND UNDER-REPRESENTED STUDENTS. THIS PROJECT WILL ADVANCE KNOWLEDGE AND PRODUCE SCIENTIFIC PRINCIPLES AND TOOLS FOR A NEW PARADIGM OF AI-IN-MEMORY COMPUTING FEATURING SIGNIFICANT IMPROVEMENTS IN ENERGY EFFICIENCY, SPEED, DYNAMICS, ROBUSTNESS, AND ON-DEVICE LEARNING CAPABILITY. THIS CROSS-LAYER PROJECT SPANS FROM DEVICE, CIRCUIT, AND ARCHITECTURE TO DNN ALGORITHM EXPLORATION. FIRST, A HYBRID RRAM-SRAM BASED IN-MEMORY COMPUTING CHIP WILL BE DESIGNED, OPTIMIZED, AND FABRICATED. SECOND, BASED ON THIS NEW COMPUTING PLATFORM, THE ON-DEVICE SPATIOTEMPORAL DYNAMIC NEURAL NETWORK STRUCTURE WILL BE DEVELOPED TO PROVIDE AN ENHANCED RUN-TIME COMPUTING PROFILE (LATENCY, RESOURCE ALLOCATION, WORKING LOAD, POWER BUDGET, ETC.), AS WELL AS IMPROVE THE ROBUSTNESS OF THE SYSTEM AGAINST HARDWARE INTRINSIC AND ADVERSARIAL NOISE INJECTION. THEN, EFFICIENT ON-DEVICE LEARNING METHODOLOGIES WITH THE DEVELOPED COMPUTING PLATFORM WILL BE INVESTIGATED. IN THE LAST THRUST, AN END-TO-END DNN TRAINING, OPTIMIZATION, MAPPING, AND EVALUATION CAD TOOL WILL BE DEVELOPED THAT INTEGRATES THE DEVELOPED HARDWARE PLATFORM AND ALGORITHM INNOVATIONS, FOR OPTIMIZING THE SOFTWARE AND HARDWARE CO-DESIGNS TO ACHIEVE THE USER-DEFINED MULTI-OBJECTIVES IN LATENCY, ENERGY EFFICIENCY, DYNAMICS, ACCURACY, ROBUSTNESS, ON-DEVICE ADAPTION, ETC. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.