The brain may be the blueprint for the next computing frontier

The article discusses the rapid advancement of neuromorphic computing, a technology that models hardware on the brain’s neurons and spiking activity to achieve highly energy-efficient and low-latency data processing. Unlike traditional deep neural networks (DNNs) that rely on continuous numeric activations and consume significant power, spiking neural networks (SNNs) use asynchronous, event-driven spikes inspired by biological neurons. This approach enables dramatic reductions in energy use and processing time; for instance, Intel’s Loihi chips reportedly perform AI inference 50 times faster and with 100 times less energy than conventional CPUs and GPUs, while IBM’s TrueNorth chip achieves unprecedented energy efficiency at 400 billion operations per second per watt. However, SNNs currently face challenges in accuracy and training tool maturity compared to traditional AI models. The global race to develop neuromorphic hardware is intensifying, with major players like Intel and IBM in the US leading early efforts through chips such as Loihi and TrueNorth, and startups