Efficient Convolutional Dataflows on Low-Power Neural Network Accelerators

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PowerLens: An Adaptive DVFS Framework for Optimizing Energy Efficiency in Deep Neural Networks

J Geng, Z Zhu, W Liu, X Zhou, B Li - Proceedings of the 61st ACM/IEEE …, 2024 - dl.acm.org
To address the power management challenges in deep neural networks (DNNs), dynamic
voltage and frequency scaling (DVFS) technology is garnering attention for its ability to …
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Reduce computational complexity for convolutional layers by skip** zeros

Z Zhang, P Zhang, Z Xu, Q Wang - 2023 IEEE 30th …, 2023 - ieeexplore.ieee.org
Convolutional neural networks necessitate good algorithms to reduce complexity, and
sufficient utilization of parallel processors for acceleration. Within convolutional layers, there …
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Split-Knit Convolution: Enabling Dense Evaluation of Transpose and Dilated Convolutions on GPUs

AM Vadakkeveedu, D Mandal… - 2022 IEEE 29th …, 2022 - ieeexplore.ieee.org
Transpose convolutions occur in several image-based neural network applications,
especially those involving segmentation or image generation. Unlike regular (forward) …
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Energy Dependence's Role in Economic Prosperity in the United States–A Participative Leadership Perspective

J Getch - 2024 - search.proquest.com
The purpose of this quantitative study is to examine energy dependence's relationship with
economic prosperity in the United States through participative leadership decisions by …
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