Efficient hardware architectures for accelerating deep neural networks: Survey
In the modern-day era of technology, a paradigm shift has been witnessed in the areas
involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep …
involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep …
Suitability of recent hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms
Computer vision and image processing algorithms form essential components of many
industrial, medical, commercial, and research-related applications. Modern imaging systems …
industrial, medical, commercial, and research-related applications. Modern imaging systems …
Quantized neural networks: Training neural networks with low precision weights and activations
The principal submatrix localization problem deals with recovering a K× K principal
submatrix of elevated mean µ in a large n× n symmetric matrix subject to additive standard …
submatrix of elevated mean µ in a large n× n symmetric matrix subject to additive standard …
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
We propose DoReFa-Net, a method to train convolutional neural networks that have low
bitwidth weights and activations using low bitwidth parameter gradients. In particular, during …
bitwidth weights and activations using low bitwidth parameter gradients. In particular, during …
ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars
A Shafiee, A Nag, N Muralimanohar… - ACM SIGARCH …, 2016 - dl.acm.org
A number of recent efforts have attempted to design accelerators for popular machine
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …
Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1
We introduce a method to train Binarized Neural Networks (BNNs)-neural networks with
binary weights and activations at run-time. At training-time the binary weights and activations …
binary weights and activations at run-time. At training-time the binary weights and activations …
[图书][B] Deep learning
An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …
conceptual background, deep learning techniques used in industry, and research …
Hardware implementation of deep network accelerators towards healthcare and biomedical applications
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …
has brought on new opportunities for applying both Deep and Spiking Neural Network …
Memory and information processing in neuromorphic systems
G Indiveri, SC Liu - Proceedings of the IEEE, 2015 - ieeexplore.ieee.org
A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …
Neumann processor architectures is the way in which memory and processing is organized …
[图书][B] Deep learning
Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …
intelligent objects, such as animated statues of human beings and tables that arrive full of …