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 …
Approximate computing: Concepts, architectures, challenges, applications, and future directions
The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …
Improving the accuracy and hardware efficiency of neural networks using approximate multipliers
Improving the accuracy of a neural network (NN) usually requires using larger hardware that
consumes more energy. However, the error tolerance of NNs and their applications allow …
consumes more energy. However, the error tolerance of NNs and their applications allow …
ALWANN: Automatic layer-wise approximation of deep neural network accelerators without retraining
The state-of-the-art approaches employ approximate computing to reduce the energy
consumption of DNN hardware. Approximate DNNs then require extensive retraining …
consumption of DNN hardware. Approximate DNNs then require extensive retraining …
Weight-oriented approximation for energy-efficient neural network inference accelerators
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 …
An improved logarithmic multiplier for energy-efficient neural computing
Multiplication is the most resource-hungry operation in neural networks (NNs). Logarithmic
multipliers (LMs) simplify multiplication to shift and addition operations and thus reduce the …
multipliers (LMs) simplify multiplication to shift and addition operations and thus reduce the …
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 …
A hardware-efficient logarithmic multiplier with improved accuracy
Logarithmic multipliers take the base-2 logarithm of the operands and perform multiplication
by only using shift and addition operations. Since computing the logarithm is often an …
by only using shift and addition operations. Since computing the logarithm is often an …
Approximate computing for ML: State-of-the-art, challenges and visions
In this paper, we present our state-of-the-art approximate techniques that cover the main
pillars of approximate computing research. Our analysis considers both static and …
pillars of approximate computing research. Our analysis considers both static and …
Energy efficient edge computing enabled by satisfaction games and approximate computing
In this paper, we introduce an energy efficient edge computing solution to collaboratively
utilize Multi-access Edge Computing (MEC) and Fully Autonomous Aerial Systems (FAAS) to …
utilize Multi-access Edge Computing (MEC) and Fully Autonomous Aerial Systems (FAAS) to …