The number of nodes typically used in sensory networks is growing rapidly, leading to large
amounts of redundant data being exchanged between sensory terminals and computing …

The recent progress in artificial intelligence (AI) and machine learning (ML) has enabled
computing platforms to solve highly complex difficult problems in computer vision, robotics …

This paper investigates the power and performance trade-offs associated with integrating
deep neural network (DNN) computation in an image sensor. The paper presents the design …

In-sensor and near-sensor computing architectures enable multiply accumulate operations
to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power …

Methods and systems for performing light measurement are disclosed. In one example, an
apparatus comprises an array of pixel cells, each pixel cell of the array of pixel cells …

The separation of the data capture and analysis in modern vision systems has led to a
massive amount of data transfer between the end devices and cloud computers, resulting in …

In one example, an apparatus comprises: an image sensor comprising an array of pixel
cells, each pixel cell including a photodiode and circuits to generate image data, the …

Conventional imaging and data processing devices are not ideal for mobile artificial vision
applications, such as vision systems for drones and robots, because of the heavy and bulky …

Reservoir computing (RC) is a powerful machine learning methodology well suited for time-
series processing. The hardware implementation of RC systems (HRC) may extend the …

Today's cameras are designed to approximate what they observe in a manner that
preserves entropy. However, time critical autonomous applications such as autonomous …