Tag: Spiking Neural Network

AI Machine Learning & Data Science Research

IEEE Publishes Comprehensive Survey of Bottom-Up and Top-Down Neural Processing System Design

An IEEE team provides a comprehensive overview of the bottom-up and top-down design approaches toward neuromorphic intelligence, highlighting the different levels of granularity present in existing silicon implementations and assessing the benefits of the different circuit design styles in neural processing systems.

AI Machine Learning & Data Science Popular Research

Toward a New Generation of Neuromorphic Computing: IBM & ETH Zurich’s Biologically Inspired Optimizer Boosts FCNN and SNN Training

IBM and ETH Zurich researchers make progress in reconciling neurophysiological insights with machine intelligence, proposing a novel biologically inspired optimizer for artificial (ANNs) and spiking neural networks (SNNs) that incorporates synaptic integration principles from biology. GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals) leads to improvements in the training time convergence, accuracy and scalability of ANNs and SNNs.

AI AIoT Machine Learning & Data Science Research

ETH Zurich Leverages Spiking Neural Networks To Build Ultra-Low-Power Neuromorphic Processors

A research team from ETH Zurich leverages existing spike-based learning circuits to propose a biologically plausible architecture that is highly successful in classifying distinct and complex spatio-temporal spike patterns. The work contributes to the design of ultra-low-power mixed-signal neuromorphic processing systems capable of distinguishing spatio-temporal patterns in spiking activity.