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 …

Continued reduction in transistor size can improve the performance of silicon integrated
circuits (ICs). However, as Moore's law approaches physical limits, high‐performance …

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 …

With the rapid advances of deep learning-based computer vision (CV) technology, digital
images are increasingly consumed, not by humans, but by downstream CV algorithms …

The human brain is a complex spiking neural network (SNN) capable of learning multimodal
signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains …

This article presents a CMOS flash light detection and ranging (LiDAR) sensor featuring an
in-pixel histogramming time-to-digital converter (hTDC) with a minimal footprint of 1110 m …

Implementing a neural network (NN) inference in a millimeter-scale system is challenging
due to limited energy and storage size. This article proposes an energy-aware adaptive NN …

This article presents a 64 64 indirect time-of-flight (iToF) image sensor with a depth range of
50 m, integrated into a 1P4M 110-nm CMOS process. The sensor is based on a single …

This paper describes a miniature edge device that performs neural network inference with
different exit options depending on available energy. In addition to the main-exit path, it …

This paper presents a synchronous 9-bit column successive approximation register (SAR)
ADC for CMOS imaging sensors. The SAR ADC uses a pseudo-differential RC DAC and a …