Custom hardware architectures for deep learning on portable devices: a review
The staggering innovations and emergence of numerous deep learning (DL) applications
have forced researchers to reconsider hardware architecture to accommodate fast and …
have forced researchers to reconsider hardware architecture to accommodate fast and …
A high-speed and low-complexity architecture for softmax function in deep learning
Recently, significant improvement has been achieved for hardware architecture design of
deep neural networks (DNNs). However, the hardware implementation of one widely used …
deep neural networks (DNNs). However, the hardware implementation of one widely used …
Pytorchfi: A runtime perturbation tool for dnns
PyTorchFI is a runtime perturbation tool for deep neural networks (DNNs), implemented for
the popular PyTorch deep learning platform. PyTorchFI enables users to perform …
the popular PyTorch deep learning platform. PyTorchFI enables users to perform …
Dl4scivis: A state-of-the-art survey on deep learning for scientific visualization
Since 2016, we have witnessed the tremendous growth of artificial intelligence+
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …
Accelerated deep learning
S Lie, M Morrison, ME James, GR Lauterbach… - US Patent …, 2020 - Google Patents
Techniques in advanced deep learning provide improvements in one or more of accuracy,
performance, and energy efficiency, such as accuracy of learning, accuracy of prediction …
performance, and energy efficiency, such as accuracy of learning, accuracy of prediction …
FPGA–accelerated CNN for real-time plant disease identification
Y Luo, X Cai, J Qi, D Guo, W Che - Computers and Electronics in …, 2023 - Elsevier
Using convolutional neural network (CNN) to identify plant diseases in-situ is a hot research
topic in smart agriculture. Due to the memory-intensive and compute-intensive …
topic in smart agriculture. Due to the memory-intensive and compute-intensive …
Optimally scheduling CNN convolutions for efficient memory access
Embedded inference engines for convolutional networks must be parsimonious in memory
bandwidth and buffer sizing to meet power and cost constraints. We present an analytical …
bandwidth and buffer sizing to meet power and cost constraints. We present an analytical …
CNN-based land cover classification combining stratified segmentation and fusion of point cloud and very high-spatial resolution remote sensing image data
K Zhou, D Ming, X Lv, J Fang, M Wang - Remote Sensing, 2019 - mdpi.com
Traditional and convolutional neural network (CNN)-based geographic object-based image
analysis (GeOBIA) land-cover classification methods prosper in remote sensing and …
analysis (GeOBIA) land-cover classification methods prosper in remote sensing and …
Wavelet representation for accelerated deep learning
S Lie, GR Lauterbach, ME James, M Morrison… - US Patent …, 2019 - Google Patents
2019-07-09 Assigned to CEREBRAS SYSTEMS INC. reassignment CEREBRAS SYSTEMS
INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS) …
INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS) …
NxMTransformer: semi-structured sparsification for natural language understanding via ADMM
Abstract Natural Language Processing (NLP) has recently achieved great success by using
huge pre-trained Transformer networks. However, these models often contain hundreds of …
huge pre-trained Transformer networks. However, these models often contain hundreds of …