ELECTRICAL SIMULATION OF CONVENTIONAL AND UNCONVENTIONAL MEMRISTIVE DEVICES AND SYSTEMS FOR NEUROMORPHIC COMPUTING

ARTIFICIAL INTELLIGENCE BASED ON NEUROMORPHIC COMPUTING IS RELENTLESSLY ARRIVING TO ALMOST EVERY CORNER OF OUR LIFE. FROM SMARTPHONES TO HOME APPLIANCES, FROM UNMANNED VEHICLES TO FACE RECOGNITION ALGORITHMS, FROM FINANCIAL DATA ANALYSIS TO YOUTUBE PREFERENCES, ALL ELECTRICAL AND COMPUTER SYSTEMS WILL INCLUDE IN THE NEAR FUTURE A PIECE OF TECHNOLOGY ABLE TO EVALUATE A COMPLEX SITUATION AND TAKE SOME KIND OF ACTION. THIS IS CURRENTLY PERFORMED USING SPECIALIZED PROCESSING UNITS AND HIGH-LEVEL COMPUTER LANGUAGES, BUT WHAT IF THE CORE COMPUTATIONS ASSOCIATED WITH THESE TASKS WERE PERFORMED BY A SINGLE CHIP AT MUCH LESS COST AND ENERGY REQUIREMENTS? WHY NOT TAKE ADVANTAGE OF THE LESSONS NATURE TEACHES US AND USE A SYSTEM THAT MIMICS THE OPERATIONS CARRIED OUT BY OUR BRAIN?, THAT IS, A SYSTEM COMPRISING ARTIFICIAL NEURONS AND SYNAPSES ABLE TO ESTABLISH CONNECTIONS AND PASS THE INFORMATION AMONG THEM. EXPERTS IN THE AREA CLAIM THAT THIS OBJECTIVE IS ALMOST AT OUR HANDS AND THAT CAN BE REACHED USING DEVICES CALLED MEMRISTORS: I.E. RESISTORS WITH MEMORY. DEVICES THAT, CONNECTED FORMING AN ARRAY OR NETWORK, ARE ABLE TO RESPOND TO A SPECIFIC ELECTRICAL STIMULUS IN THE SENSE THEY WERE PREVIOUSLY TAUGHT OR MORE ADVANCED, DEVICES THAT CAN LEARN FROM NEW EXPERIENCES OR DATA. MEMRISTIVE DEVICES AND THEIR ELECTRICAL ACTIVITY WITHIN THE FRAMEWORK OF NEUROMORPHIC COMPUTING IS THE CENTRAL TOPIC OF OUR RESEARCH. OUR PROJECT INVOLVES FOUR WELL-DEFINED ACTIVITIES: FIRST, THE IMPROVEMENT, EXTENSION, AND APPLICATION OF A RECENTLY DEVELOPED COMPACT MODEL FOR MEMRISTORS CALLED MEMDIODE. THIS INCLUDES THE INCORPORATION OF EFFECTS RELATED TO THE TEMPERATURE, FREQUENCY, VARIABILITY, FLUCTUATIONS, DEGRADATION, ETC. THE MODEL WILL BE APPLIED TO THE ELECTRICAL MODELING OF DIFFERENT KINDS OF NEURAL NETWORKS AS WELL AS TO SPECIFIC UNIVERSAL APPLICATIONS. SECOND, A COMPLETE SIMULATION PLATFORM WHICH COMBINES PYTHON WITH LTSPICE WILL BE DEVELOPED. THIS PLATFORM WILL CONTROL DE FLOW OF INFORMATION, THE CIRCUITAL ANALYSIS AND THE STATISTICAL TREATMENT OF THE OUTPUT INFORMATION. THIRD, WE WILL EXPLORE BRAIN-INSPIRED AND QUANTUM MEMRISTIVE SYSTEMS. IN THIS CASE, SELF-ORGANIZED RANDOM NETWORKS REPRESENTING COMPLEX SYSTEMS WHERE AN EMERGENT BEHAVIOR ARISE FROM THE BEHAVIOR OF A MULTITUDE OF SINGLE FUNDAMENTAL UNITS ACTING AS SYNAPTIC CONNECTIONS WILL BE INVESTIGATED. MEMRISTIVE DEVICES EXHIBITING QUANTIZED CONDUCTANCE LEVELS ALSO REPRESENT PROMISING PLATFORMS FOR THE REALIZATION OF QUANTUM-BASED STANDARDS OF RESISTANCE WORKING IN AIR AT ROOM TEMPERATURE. FINALLY, WE WILL ALSO EXPLORE THE APPLICATION OF MEMRISTIVE DEVICES TO CHAOTIC AND COMPLEX SYSTEMS, FOCUSING ON BIO-INSPIRED SYSTEMS FOR SENSING PURPOSES.