This perspective article outlines the path toward widespread commercial adoption of Neuromorphic (NM) processors by addressing historical barriers in programming models and hardware reliability. It highlights that gradient-based training now enables general-purpose applications on spiking neural networks, while digital CMOS implementations improve robustness over analog designs. The text identifies key markets for NM technology: low-power edge inference (TinyML), IoT devices, wearables, and real-time sensory processing like audio keyword spotting. Benchmark data indicates significant energy efficiency gains compared to GPUs and CPUs across various tasks. Standardization of interfaces, software toolchains, and benchmarks is recommended to facilitate industry uptake alongside established architectures.
Use-case examples
- IoT devices
- wearables
- audio keyword spotting
- vision processing
- TinyML inference