Neuromorphic Architecture

Neuromorphic architecture aims to build computing systems inspired by the brain's structure and function, prioritizing energy efficiency and parallel processing over traditional von Neumann architectures. Current research heavily focuses on spiking neural networks (SNNs), particularly convolutional SNNs, exploring novel learning algorithms like variations of spike-timing-dependent plasticity (STDP) and optimizing their implementation on neuromorphic hardware platforms like Loihi and BrainScaleS. This approach holds significant promise for low-power applications in areas such as edge computing, real-time signal processing (audio and video), and potentially even solving complex optimization problems, offering a compelling alternative to conventional deep learning methods.