Analog Circuits Enabling Learning in Mixed-Signal Neuromorphic SNNs, With Tristate Stability and Weight Discretization Circuits


A technical paper titled “Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks” was published by researchers at University of Zurich and ETH Zurich. Abstract: "Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circui... » read more

Analog On-Chip Learning Circuits In Mixed-Signal Neuromorphic SNNs


A technical paper titled "Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks" was published by researchers at Institute of Neuroinformatics, University of Zurich, and ETH Zurich. Abstract: "Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their s... » read more