Large Scale Neuromorphic

Large-scale neuromorphic computing aims to build hardware systems mimicking the brain's structure and function for energy-efficient, high-performance computation. Current research focuses on developing advanced spiking neural network (SNN) architectures, including efficient neuron and synapse implementations in CMOS and memristive technologies, and novel learning algorithms like event-based backpropagation to overcome limitations posed by communication delays. These advancements are driving progress towards larger-scale neuromorphic chips with improved accuracy and speed for applications in machine learning and beyond, offering a potential paradigm shift in computing efficiency.