A survey on optimized implementation of deep learning models on the nvidia jetson platform
S Mittal - Journal of Systems Architecture, 2019 - Elsevier
Abstract Design of hardware accelerators for neural network (NN) applications involves
walking a tight rope amidst the constraints of low-power, high accuracy and throughput …
walking a tight rope amidst the constraints of low-power, high accuracy and throughput …
A comprehensive review of convolutional neural networks for defect detection in industrial applications
Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …
Edge intelligence: Empowering intelligence to the edge of network
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …
caching, processing, and analysis proximity to where data are captured based on artificial …
Edge intelligence: Architectures, challenges, and applications
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …
caching, processing, and analysis in locations close to where data is captured based on …
A Survey of Design and Optimization for Systolic Array-based DNN Accelerators
In recent years, it has been witnessed that the systolic array is a successful architecture for
DNN hardware accelerators. However, the design of systolic arrays also encountered many …
DNN hardware accelerators. However, the design of systolic arrays also encountered many …
High-order tensor flow processing using integrated photonic circuits
Tensor analytics lays the mathematical basis for the prosperous promotion of multiway
signal processing. To increase computing throughput, mainstream processors transform …
signal processing. To increase computing throughput, mainstream processors transform …
DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor
Z Xu, M Luo, W Lin, G Xue, P Wang, X **… - Briefings in …, 2021 - academic.oup.com
Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly
benefit vaccine development and cancer immunotherapy. However, identifying …
benefit vaccine development and cancer immunotherapy. However, identifying …
A survey of SRAM-based in-memory computing techniques and applications
As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …
movement overheads, researchers have started exploring in-memory computing (IMC) …
A survey on the optimization of neural network accelerators for micro-ai on-device inference
Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
Adaptable butterfly accelerator for attention-based NNs via hardware and algorithm co-design
Attention-based neural networks have become pervasive in many AI tasks. Despite their
excellent algorithmic performance, the use of the attention mechanism and feedforward …
excellent algorithmic performance, the use of the attention mechanism and feedforward …