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 …
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 …
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 …
Targeting dnn inference via efficient utilization of heterogeneous precision dnn accelerators
Modern applications rely more and more on the simultaneous execution of multiple DNNs,
and Heterogeneous DNN Accelerators (HDAs) prevail as a solution to this trend. In this …
and Heterogeneous DNN Accelerators (HDAs) prevail as a solution to this trend. In this …
Approximate computing and the efficient machine learning expedition
Approximate computing (AxC) has been long accepted as a design alternative for efficient
system implementation at the cost of relaxed accuracy requirements. Despite the AxC …
system implementation at the cost of relaxed accuracy requirements. Despite the AxC …
Positive/negative approximate multipliers for DNN accelerators
O Spantidi, G Zervakis… - 2021 IEEE/ACM …, 2021 - ieeexplore.ieee.org
Recent Deep Neural Networks (DNNs) manage to deliver superhuman accuracy levels on
many AI tasks. DNN accelerators are becoming integral components of modern systems-on …
many AI tasks. DNN accelerators are becoming integral components of modern systems-on …
Co-design of approximate multilayer perceptron for ultra-resource constrained printed circuits
Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive
manufacturing process, enabling machine learning (ML) applications for domains that …
manufacturing process, enabling machine learning (ML) applications for domains that …
Adaptable approximate multiplier design based on input distribution and polarity
Approximate computing is an efficient approach to reduce the design complexity for error-
resilient applications. Multipliers are key arithmetic units in many applications, such as deep …
resilient applications. Multipliers are key arithmetic units in many applications, such as deep …
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization
Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of
domains. However, DNNs are becoming computationally intensive and energy hungry at an …
domains. However, DNNs are becoming computationally intensive and energy hungry at an …
Energy-efficient dnn inference on approximate accelerators through formal property exploration
O Spantidi, G Zervakis… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep neural networks (DNNs) are being heavily utilized in modern applications, putting
energy-constraint devices to the test. To bypass high energy consumption issues …
energy-constraint devices to the test. To bypass high energy consumption issues …