In artificial intelligence, the large role is played by machine learning (ML) in a variety of
applications. This article aims at providing a comprehensive survey on summarizing recent …

This article addresses the growing need in resource-constrained edge computing scenarios
for energy-efficient convolutional neural network (CNN) accelerators on mobile Field …

The design of convolutional neural network (CNN) hardware accelerators based on a single
computing engine (CE) architecture or multi-CE architecture has received widespread …

In recent years, convolutional neural networks (CNNs) have gained widespread adoption in
remote sensing image processing. Deploying CNN-based algorithms on satellite edge …

This paper proposes a Design Space Exploration for Edge machine learning through the
utilization of the novel MathWorks FPGA Deep Learning Processor IP, featured in the HDL …

From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance
systems to seamlessly integrating with augmented reality, object detection algorithms stand …

This paper presents the design and implementation of a hardware system for real-time
object detection and range estimation utilizing Frequency-Modulated Continuous Wave …

Lightweight convolutional neural networks (CNNs) enable lower inference latency and data
traffic, facilitating deployment on resource-constrained edge devices such as field …

Field programmable gate arrays (FPGAs) have gained recognition as a suitable platform for
implementing Convolutional Neural Network (CNN)-based algorithms due to their favorable …

This paper tackles the challenges of implementing few-shot learning on embedded systems,
specifically FPGA SoCs, a vital approach for adapting to diverse classification tasks …