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 …
Accelerating CNN inference on ASICs: A survey
D Moolchandani, A Kumar, SR Sarangi - Journal of Systems Architecture, 2021 - Elsevier
Convolutional neural networks (CNNs) have proven to be a disruptive technology in most
vision, speech and image processing tasks. Given their ubiquitous acceptance, the research …
vision, speech and image processing tasks. Given their ubiquitous acceptance, the research …
Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network
Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous
compute intensity. Fully realizing the potential of acceleration in this domain requires …
compute intensity. Fully realizing the potential of acceleration in this domain requires …
Channelnets: Compact and efficient convolutional neural networks via channel-wise convolutions
Convolutional neural networks (CNNs) have shown great capability of solving various
artificial intelligence tasks. However, the increasing model size has raised challenges in …
artificial intelligence tasks. However, the increasing model size has raised challenges in …
A real-time convolutional neural network for super-resolution on FPGA with applications to 4K UHD 60 fps video services
In this paper, we present a novel hardware-friendly super-resolution (SR) method based on
a convolutional neural network (CNN) and its dedicated hardware (HW) on field …
a convolutional neural network (CNN) and its dedicated hardware (HW) on field …
Mst-compression: Compressing and accelerating binary neural networks with minimum spanning tree
Binary neural networks (BNNs) have been widely adopted to reduce the computational cost
and memory storage on edge-computing devices by using one bit representation for …
and memory storage on edge-computing devices by using one bit representation for …
An energy-efficient reconfigurable processor for binary-and ternary-weight neural networks with flexible data bit width
Due to less memory requirement, low computation overhead and negligible accuracy
degradation, deep neural networks with binary/ternary weights (BTNNs) have been widely …
degradation, deep neural networks with binary/ternary weights (BTNNs) have been widely …
Nand-net: Minimizing computational complexity of in-memory processing for binary neural networks
Popular deep learning technologies suffer from memory bottlenecks, which significantly
degrade the energy-efficiency, especially in mobile environments. In-memory processing for …
degrade the energy-efficiency, especially in mobile environments. In-memory processing for …
Automatic group-based structured pruning for deep convolutional networks
H Wei, Z Wang, G Hua, J Sun, Y Zhao - IEEE Access, 2022 - ieeexplore.ieee.org
Structured pruning methods have been used in several convolutional neural networks
(CNNs). However, group-based structured pruning is a challenging task. In previous …
(CNNs). However, group-based structured pruning is a challenging task. In previous …
Towards fast and energy-efficient binarized neural network inference on fpga
Binarized Neural Network (BNN) removes bitwidth redundancy in classical CNN by using a
single bit (-1/+ 1) for network parameters and intermediate representations, which has …
single bit (-1/+ 1) for network parameters and intermediate representations, which has …