Currently, AI and edge computing are hitting a wall when it comes to power budgets, latency, and cloud dependence, hindering real-world deployments. This issue is complicated by the need to integrate a great deal of input from sensors and other devices.
Sensors capture rich signals, but current logic solutions have trouble keeping up efficiently. Neuromorphic computing helps address this bottleneck, providing brain-inspired, event-driven intelligence for enhanced sensor integration.
A spiking neural network’s (SNN) extremely low-power operation allows for sensor data processing in microwatt and nanowatt ranges, with real-time responsiveness and inherently low processing latency. It also enables privacy by design through on-device data processing, with compact and scalable architectures optimized for embedded systems. Neuromorphic computing offers robust, adaptive performance resilient to noise and sparse signal activity.
Innatera’s Spiking Neural Processor architecture operates with significantly lower energy than conventional edge AI, leading the way to truly always-on sensing in devices. In this podcast, we talk to Sumeet Kumar, Co-founder & CEO of Innatera, about the state of the AI industry and the company’s SNN technology.
