👉 Sleep computing, also known as neuromorphic computing or brain-inspired computing, is a paradigm that mimics the structure and function of the human brain to process information efficiently. Unlike traditional computing, which relies on sequential processing and discrete steps, sleep computing uses spiking neural networks that operate asynchronously and in parallel, much like neurons in the brain. These networks process information in discrete packets of data called spikes, which can represent both the presence and absence of signals, allowing for highly energy-efficient computation. This approach excels in tasks requiring real-time processing, pattern recognition, and learning from sparse data, making it particularly suitable for applications like edge computing, robotics, and sensory processing where power consumption and speed are critical. By emulating the brain's architecture, sleep computing aims to create more adaptive, intelligent, and resource-efficient systems.
