Design of Low-Power Neuromorphic Architectures for IoT Applications

The rapid growth of the Internet of Things (IoT) demands computing systems that remain highly intelligent while adhering to tight energy constraints. For always-on edge applications, traditional processors are too power-hungry. This neuromorphic system is developed for IoT to achieve computing integrity and has remarkable efficiency. We propose computer-memory architecture which essentially is event-driven processing and temporal-spike coding. Architectural breakthroughs include clockless processing and adaptive precision and therefore exploit temporality with hierarchical event encoding. While current hardware solutions show a power consumption of 70 μW to 680 μW, our system shows 10 – 100× improvement in overall efficiency. Testing proved that with radar gesture recognition, audio pattern matching and visual event detection, we have more than 96 % accuracy. Inference energy is 1.38 nJ, and molecular operation cost is 9.9 pJ of the architecture with these efficient metrics, a new family of autonomous IoT applications can be developed, from battery-free sensor networks to implantable devices that run for years off a single charge.