Recent advances in convolutional neural network acceleration
In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …
various fields such as image classification, pattern recognition, and multi-media …
A survey of FPGA-based accelerators for convolutional neural networks
S Mittal - Neural computing and applications, 2020 - Springer
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …
wide range of cognitive tasks, and due to this, they have received significant interest from the …
A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection
Convolutional neural networks (CNNs) require numerous computations and external
memory accesses. Frequent accesses to off-chip memory cause slow processing and large …
memory accesses. Frequent accesses to off-chip memory cause slow processing and large …
DNNBuilder: An automated tool for building high-performance DNN hardware accelerators for FPGAs
Building a high-performance FPGA accelerator for Deep Neural Networks (DNNs) often
requires RTL programming, hardware verification, and precise resource allocation, all of …
requires RTL programming, hardware verification, and precise resource allocation, all of …
A survey of FPGA-based neural network accelerator
Recent researches on neural network have shown significant advantage in machine
learning over traditional algorithms based on handcrafted features and models. Neural …
learning over traditional algorithms based on handcrafted features and models. Neural …
A CNN accelerator on FPGA using depthwise separable convolution
Convolutional neural networks (CNNs) have been widely deployed in the fields of computer
vision and pattern recognition because of their high accuracy. However, large convolution …
vision and pattern recognition because of their high accuracy. However, large convolution …
Flextensor: An automatic schedule exploration and optimization framework for tensor computation on heterogeneous system
Tensor computation plays a paramount role in a broad range of domains, including machine
learning, data analytics, and scientific computing. The wide adoption of tensor computation …
learning, data analytics, and scientific computing. The wide adoption of tensor computation …
Recent advances in efficient computation of deep convolutional neural networks
Deep neural networks have evolved remarkably over the past few years and they are
currently the fundamental tools of many intelligent systems. At the same time, the …
currently the fundamental tools of many intelligent systems. At the same time, the …
Hw-nas-bench: Hardware-aware neural architecture search benchmark
HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous
attention by automating the design of DNNs deployed in more resource-constrained daily …
attention by automating the design of DNNs deployed in more resource-constrained daily …
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 …