A systematic literature review on hardware implementation of artificial intelligence algorithms
Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent
evolution of smart systems. AI solutions are pushing towards a significant shift in many fields …
evolution of smart systems. AI solutions are pushing towards a significant shift in many fields …
Design possibilities and challenges of DNN models: a review on the perspective of end devices
Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …
resource-constrained devices have become abundant in recent years. As of now, the …
FINN-R An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
Convolutional Neural Networks have rapidly become the most successful machine-learning
algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded …
algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded …
Review of neural network model acceleration techniques based on FPGA platforms
F Liu, H Li, W Hu, Y He - Neurocomputing, 2024 - Elsevier
Neural network models, celebrated for their outstanding scalability and computational
capabilities, have demonstrated remarkable performance across various fields such as …
capabilities, have demonstrated remarkable performance across various fields such as …
Cloudsatnet-1: Fpga-based hardware-accelerated quantized cnn for satellite on-board cloud coverage classification
CubeSats, the nanosatellites and microsatellites with a wet mass up to 60 kg, accompanied
by the cost decrease of accessing the space, amplified the rapid development of the Earth …
by the cost decrease of accessing the space, amplified the rapid development of the Earth …
FPGA-based implementation of classification techniques: A survey
Recently, a number of classification techniques have been introduced. However, processing
large dataset in a reasonable time has become a major challenge. This made classification …
large dataset in a reasonable time has become a major challenge. This made classification …
PIR-DSP: An FPGA DSP block architecture for multi-precision deep neural networks
SR Rasoulinezhad, H Zhou, L Wang… - 2019 IEEE 27th …, 2019 - ieeexplore.ieee.org
Quantisation is a key optimisation strategy to improve the performance of floating-point deep
neural network (DNN) accelerators. Digital signal processing (DSP) blocks on field …
neural network (DNN) accelerators. Digital signal processing (DSP) blocks on field …
Accelerating deep neural networks implementation: A survey
Abstract Recently, Deep Learning (DL) applications are getting more and more involved in
different fields. Deploying such Deep Neural Networks (DNN) on embedded devices is still a …
different fields. Deploying such Deep Neural Networks (DNN) on embedded devices is still a …
Environmental sound recognition on embedded systems: From FPGAs to TPUs
In recent years, Environmental Sound Recognition (ESR) has become a relevant capability
for urban monitoring applications. The techniques for automated sound recognition often …
for urban monitoring applications. The techniques for automated sound recognition often …
Samo: Optimised map** of convolutional neural networks to streaming architectures
Significant effort has been placed on the development of toolflows that map Convolutional
Neural Network (CNN) models to Field Programmable Gate Arrays (FPGAs) with the aim of …
Neural Network (CNN) models to Field Programmable Gate Arrays (FPGAs) with the aim of …