Smart cameras everywhere: AI vision on edge with emerging memories This focus paper provides an overview on the emerging area of inmemory neural vision chips for building intelligent cameras. The generalization abilities of neural networks and their close proximity to eye and sensory processing units in human brain lays foundations for building neural vision chips capable of performing object detection, classification, and predictions. In realtime settings, often the information visible to a camera does not meaningfully capture all the relevant context, object dynamics, and behaviors, making smart camera solutions complex and energy hungry. Learning from images is a continuous process in the human brain while consuming extremely low energy, and these properties have proven to be the benchmarks of the ideal neural vision chips. Offloading the intelligent data processing and image processing to edge devices using neural vision chips offers several advantages of speed, lower energy costs, and security. In practice, with emerging memory arrays, memory and computing become indistinguishable that can perform dotproduct computations, store weights, and perform logic operations forming the basic blocks of building several types of neural networks useful for solving a range of computer vision problems.

Artificial Vision on Edge IoT Devices: A Practical Case for 3D Data Classification Nowadays, with the huge advance of sensor technology and the increase of the amount of data generated by them, techniques have to be developed to be able to process all this amount of information in realtime applications on edge devices, close to where data is being generated. If all that information has to be sent to the cloud to be processed, it has certain disadvantages in terms of latency, bandwidth, privacy and reliability, compared to locally processing it on the edge. In this paper, the implementation of deep learning algorithms in low power and limited resources devices in an Internet of Things scenario is studied. In order to work in realtime applications, the influence of different low power consumption deep learning hardware accelerators is studied. Finally, a practical case for smart farming is shown with comparative results in terms of power consumption and performance when running the same artificial vision algorithm on different devices.