On the opportunities of green computing: A survey
Artificial Intelligence (AI) has achieved significant advancements in technology and research
with the development over several decades, and is widely used in many areas including …
with the development over several decades, and is widely used in many areas including …
Mixed-precision quantization for federated learning on resource-constrained heterogeneous devices
While federated learning (FL) systems often utilize quantization to battle communication and
computational bottlenecks they have heretofore been limited to deploying fixed-precision …
computational bottlenecks they have heretofore been limited to deploying fixed-precision …
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
Bio-inspired Spiking Neural Networks (SNN) are now demonstrating comparable accuracy
to intricate convolutional neural networks (CNN), all while delivering remarkable energy and …
to intricate convolutional neural networks (CNN), all while delivering remarkable energy and …
Toward High-Accuracy, Programmable Extreme-Edge Intelligence for Neuromorphic Vision Sensors utilizing Magnetic Domain Wall Motion-based MTJ
The desire to empower resource-limited edge devices with computer vision (CV) must
overcome the high energy consumption of collecting and processing vast sensory data. To …
overcome the high energy consumption of collecting and processing vast sensory data. To …
VitBit: Enhancing Embedded GPU Performance for AI Workloads through Register Operand Packing
The rapid advancement of Artificial Intelligence (AI) necessitates significant enhancements
in the energy efficiency of Graphics Processing Units (GPUs) for Deep Neural Network …
in the energy efficiency of Graphics Processing Units (GPUs) for Deep Neural Network …
Toward High Performance, Programmable Extreme-Edge Intelligence for Neuromorphic Vision Sensors utilizing Magnetic Domain Wall Motion-based MTJ
The desire to empower resource-limited edge devices with computer vision (CV) must
overcome the high energy consumption of collecting and processing vast sensory data. To …
overcome the high energy consumption of collecting and processing vast sensory data. To …
Accelerating Inference of Networks in the Frequency Domain
It has been demonstrated that networks' parameters can be significantly reduced in the
frequency domain with a very small decrease in accuracy. However, given the cost of …
frequency domain with a very small decrease in accuracy. However, given the cost of …
Timesteps meet Bits: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks with ANN-to-SNN Conversion
Spiking Neural Networks (SNN) are now demonstrating comparable accuracy to intricate
convolutional neural networks (CNN), all while delivering remarkable energy and latency …
convolutional neural networks (CNN), all while delivering remarkable energy and latency …