Hardware approximate techniques for deep neural network accelerators: A survey
Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Weight-oriented approximation for energy-efficient neural network inference accelerators
ZG Tasoulas, G Zervakis… - … on Circuits and …, 2020 - ieeexplore.ieee.org
Current research in the area of Neural Networks (NN) has resulted in performance
advancements for a variety of complex problems. Especially, embedded system applications …
advancements for a variety of complex problems. Especially, embedded system applications …
The effects of approximate multiplication on convolutional neural networks
This article analyzes the effects of approximate multiplication when performing inferences on
deep convolutional neural networks (CNNs). The approximate multiplication can reduce the …
deep convolutional neural networks (CNNs). The approximate multiplication can reduce the …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
Libraries of approximate circuits: Automated design and application in CNN accelerators
Libraries of approximate circuits are composed of fully characterized digital circuits that can
be used as building blocks of energy-efficient implementations of hardware accelerators …
be used as building blocks of energy-efficient implementations of hardware accelerators …
[HTML][HTML] Association between urinary metals and leukocyte telomere length involving an artificial neural network prediction: findings based on NHANES 1999–2002
F Xia, Q Li, X Luo, J Wu - Frontiers in Public Health, 2022 - frontiersin.org
Objective Leukocytes telomere length (LTL) was reported to be associated with cellular
aging and aging related disease. Urine metal also might accelerate the development of …
aging and aging related disease. Urine metal also might accelerate the development of …
NASCaps: A framework for neural architecture search to optimize the accuracy and hardware efficiency of convolutional capsule networks
Deep Neural Networks (DNNs) have made significant improvements to reach the desired
accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently …
accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently …
Adapt: Fast emulation of approximate dnn accelerators in pytorch
Current state-of-the-art employs approximate multipliers to address the highly increased
power demands of deep neural network (DNN) accelerators. However, evaluating the …
power demands of deep neural network (DNN) accelerators. However, evaluating the …
Design automation of approximate circuits with runtime reconfigurable accuracy
Leveraging the inherent error tolerance of a vast number of application domains that are
rapidly growing, approximate computing arises as a design alternative to improve the …
rapidly growing, approximate computing arises as a design alternative to improve the …