Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and
been widely applied in a variety of applications, including FPGAs, image processing, and …

Recently, artificial intelligence applications have become part of almost all emerging
technologies around us. Neural networks, in particular, have shown significant advantages …

Logic synthesis is a fundamental step in hardware design whose goal is to find structural
representations of Boolean functions while minimizing delay and area. If the function is …

Convolutional Neural Networks (CNNs) are extensively used in a wide range of
applications, commonly including computer vision tasks like image and video classification …

Fast and resource-efficient inference in artificial neural networks (ANNs) is of utmost
importance and drives many new developments in the area of new hardware architectures …

The ever-growing computational demands of increasingly complex machine learning
models frequently necessitate the use of powerful cloud-based infrastructure for their …

Binary neural networks (BNNs) have attracted significant interest for the implementation of
deep neural networks (DNNs) on resource-constrained edge devices, and various BNN …

Field-programmable gate arrays (FPGAs) are widely used locally to speed up deep neural
network (DNN) algorithms with high computational throughput and energy efficiency …