Project Grant 2340799

CAREER: SHF: BIO-INSPIRED MICROSYSTEMS FOR ENERGY-EFFICIENT REAL-TIME SENSING, DECISION, AND ADAPTATION -CONTEMPORARY ARTIFICIAL INTELLIGENCE SYSTEMS TYPICALLY DO NOT ADAPT TO THEIR ENVIRONMENT IN REAL TIME, ARE VERY POWER HUNGRY, AND ARE TYPICALLY DEVELOPED IN ISOLATION FROM THE UNDERLYING HARDWARE. HOWEVER, BIOLOGICAL INTELLIGENCE HAS ADDRESSED THESE LIMITATIONS, BEING ENERGY-EFFICIENT, ADAPTABLE, AND WELL-INTEGRATED WITH THE UNDERLYING SUBSTRATE. USING BIOLOGICAL INTELLIGENCE AS THE GUIDING PRINCIPLE, THIS PROJECT DEVELOPS MICROELECTRONIC SYSTEMS THAT CAN EFFICIENTLY, SENSE PHYSICAL SIGNALS FROM THEIR ENVIRONMENT, MAKE REAL-TIME DECISIONS, AND ADAPT AND LEARN WITH MINIMAL ENERGY USAGE. BY DRAWING INSPIRATION FROM BIOLOGY, THIS PROJECT BLURS THE DISTINCTION BETWEEN SENSING, COMPUTATION, AND ALGORITHM BY SEAMLESS INTEGRATION OF MEMORY, COMPUTING, AND SENSING AND A LEARNING ALGORITHM. THROUGH MIRRORING THIS BIOLOGICAL MODEL, THE PROJECT SEEKS TO DEVELOP THE NEXT GENERATION AUTONOMOUS INTELLIGENT SYSTEMS WITH APPLICATIONS RANGING FROM SMARTER CELLPHONES TO IMPROVED BRAIN-MACHINE INTERFACES. THE OUTLINED EDUCATIONAL ACTIVITIES WILL ENABLE COLLABORATION WITH INDUSTRIAL PARTNERS AND HISTORICALLY BLACK COLLEGES AND UNIVERSITIES TO ENABLE CUTTING EDGE MICROELECTRONIC EDUCATION TO ENABLE THE DOMESTIC WORKFORCE TO MEET THE STRATEGIC SEMICONDUCTOR NEEDS OF THE NATION. THE PROJECT CO-DESIGNS CONTINUAL LEARNING ALGORITHMS WITH CUTTING-EDGE, ENERGY-EFFICIENT, MICROELECTRONIC DESIGNS THAT LEVERAGE EMERGING DEVICES IN THE FORM OF FERROELECTRIC FIELD-EFFECT TRANSISTORS (FEFETS) TO ENABLE NEXT-GENERATION, ENERGY-EFFICIENT, ADAPTIVE HARDWARE FOR SENSING, DECISION-MAKING, AND LEARNING. ANALOG-TO-FEATURE CONVERTER FRONT-END SYSTEMS LEVERAGING FEFETS AS PROGRAMMABLE TRANSCONDUCTANCES WILL BE DESIGNED TO ACQUIRE ANALOG INPUT AND EXTRACT PERTINENT LEARNED FEATURES. THESE SUBSYSTEMS WILL FEED DOWNSTREAM FEFET-BASED COMPUTE-IN-MEMORY (CIM) CIRCUITS WITH CUSTOM-DESIGNED CIRCUITS TO ALLEVIATE THE ANALOG-TO-DIGITAL CONVERTER BOTTLENECK CURRENTLY LIMITING MOST CIM ARCHITECTURES. STATIC RANDOM ACCESS MEMORY WILL AUGMENT FEFET STRUCTURES TO ENABLE ON-CHIP LEARNING AND DYNAMIC RECONFIGURABILITY. IN LOCKSTEP WITH THE UNDERLYING HARDWARE, TAILORED CONTINUAL LEARNING ALGORITHMS WILL BE CO-DESIGNED WITH THE ANALOG-TO-DIGITAL CONVERTERS AND THE FEFET ARRAY TO ENDOW THE SYSTEM WITH ENERGY-EFFICIENT RESILIENCE AND ADAPTATION. MICROELECTRONIC SYSTEMS DESIGNED USING THE PRESENTED APPROACH COULD SEE WIDE RANGING APPLICATIONS FROM BRAIN-COMPUTER-INTERFACES AND IMPLANTABLE SYSTEMS TO BLIND WAVEFORM CLASSIFICATION FOR WIRELESS SYSTEMS. TO VALIDATE THE APPROACH, AN INTEGRATED CIRCUIT WILL BE FABRICATED AND MEASURED. THESE WILL ALSO SERVE TO PROVIDE DATA TO FURTHER REFINE AND CALIBRATE SOFTWARE MODELS FOR PERFORMANCE EVALUATION AND DESIGN-SPACE EXPLORATION. THIS PROJECT WILL ULTIMATELY DEVELOP COMPONENTS CRITICAL FOR BIOLOGICALLY INSPIRED, ENERGY-EFFICIENT, AUTONOMOUS AGENTS. 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.