Energy-efficient neural image processing for Internet-of-Things edge devices

Enhancing energy/resource efficiency of neural networks is critical to support on-chip neural image processing at Internet-of-Things edge devices. This paper presents recent technology advancements towards energy-efficient neural image processing. 3D integration of image sensor and neural network improves power-efficiency with programmability and scalability. Computation energy of feedforward and recurrent neural networks is reduced by dynamic control of approximation, and storage demand is reduced by image-based adaptive weight compression. Emerging devices such as tunnel FET and Resistive Random Access Memory are utilized to achieve higher computation efficiency than CMOS-based designs.