Mixed Signal Neuromorphic

Mixed-signal neuromorphic computing aims to create energy-efficient computing systems by combining the strengths of analog and digital circuits to mimic the brain's architecture. Current research focuses on developing accurate simulation frameworks for mixed-signal neuromorphic hardware, optimizing analog-to-digital converters (ADCs) for specific tasks using memristors, and designing efficient spiking neural networks (SNNs) with on-chip learning capabilities. This approach holds significant promise for advancing both fundamental neuroscience understanding and practical applications in edge computing and low-power AI, particularly through improved accuracy and reduced energy consumption compared to traditional digital approaches.