Multiplication is one of the essential functions in all digital systems. The evaluation of digital
system, have brought out new challenges in VLSI (Very Large Scale Integration) designing …

Autonomous vehicles are at the forefront of future transportation solutions, but their success
hinges on reliable perception. This review paper surveys image processing and sensor …

Deploying deep learning models on embedded systems for computer vision tasks has been
challenging due to limited compute resources and strict energy budgets. The majority of …

In-memory computing using emerging technologies such as resistive random-access
memory (ReRAM) addresses the 'von Neumann bottleneck'and strengthens the present …

Object detection is an important area in self-driving automotive. The YOLO algorithm and its
well-embedded implementation is a promising solution for object detection. In this paper, a …

Edge devices offer advantages such as low computation latency and high data security for
executing convolutional neural networks (CNNs). However, deploying CNNs on resource …

Practical implementation of deep neural networks (DNNs) demands significant hardware
resources, necessitating high computational power and memory bandwidth. While existing …

To address the challenge of deploying convolutional neural networks (CNNs) on edge
devices with limited resources, this article presents an effective 4-bit quantization scheme for …

Traditionally, the usual multipliers are used to multiply signals by a constant, but
multiplication by a constant can be considered as a special operation requiring the …

In response to the escalating demand for hardware-efficient Deep Neural Network (DNN)
architectures, we present a novel quantize-enabled multiply-accumulate (MAC) unit. Our …