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Chapter Eight - Energy-efficient deep learning inference on edge devices
Advances in Computing, 2021The success of deep learning comes at the cost of very high computational complexity. Consequently, Internet of Things (IoT) edge nodes typically offload deep learning tasks to powerful cloud servers, an inherently inefficient solution. In fact, transmitting raw data to the cloud through wireless links incurs long latencies and high energy consumption.
Francesco Daghero +2 more
semanticscholar +3 more sources
Towards ADC-Less Compute-In-Memory Accelerators for Energy Efficient Deep Learning
Design, Automation and Test in Europe, 2022Compute-in-Memory (CiM) hardware has shown great potential in accelerating Deep Neural Networks (DNNs). However, most CiM accelerators for matrix vector multiplication rely on costly analog to digital converters (ADCs) which becomes a bottleneck in ...
Utkarsh Saxena, I. Chakraborty, K. Roy
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An Energy-Efficient Hardware Accelerator for Hierarchical Deep Reinforcement Learning
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021Reinforcement Learning (RL) has shown great performance in solving sequential decision-making and control in dynamic environments problems. Despite its achievements, training Deep Neural Network (DNN) based RL is expensive in terms of time and power because of the large number of episodes required to train agents with high dimensional image ...
Aidin Shiri +5 more
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Energy-Efficient Deep Reinforcement Learning Assisted Resource Allocation for 5G-RAN Slicing
IEEE Transactions on Vehicular Technology, 2022One of the pillars of the 5G architecture is network slicing, in which hardware, radio, and power resources are virtualized as a logical network taking into account the requirements of diverse applications.
Y. Azimi +3 more
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Energy-efficient VM scheduling based on deep reinforcement learning
Future Generation Computer Systems, 2021Abstract Achieving data center resource optimization and QoS guarantee driven by high energy efficiency has become a research hotspot. However, QoS information directly sampled from the cloud environment will inevitably be affected by a small amount of structured noise.
Bin Wang 0048 +2 more
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Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning
2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019Large scale deep neural networks (DNNs) have achieved remarkable successes in various artificial intelligence applications. However, high computational complexity and energy costs of DNNs impede their deployment on edge devices with a limited energy ...
Chengyue Gong +5 more
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Deep Learning for Energy Efficient Beamforming in MU-MISO Networks: A GAT-Based Approach
IEEE Wireless Communications Letters, 2023This letter investigates the deep learning enabled energy efficient beamforming design for multi-user (MU) multiple-input single-output (MISO) networks.
Yu-Hang Li +4 more
semanticscholar +1 more source
DNPU: An Energy-Efficient Deep-Learning Processor with Heterogeneous Multi-Core Architecture
IEEE Micro, 2018An energy-efficient deep-learning processor called DNPU is proposed for the embedded processing of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in mobile platforms.
Dongjoo Shin +4 more
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Booth Encoding-Based Energy Efficient Multipliers for Deep Learning Systems
IEEE Transactions on Circuits and Systems - II - Express Briefs, 2023Artificial intelligence on edge is a growing research field. In this brief, we propose a novel re-encoding scheme for reducing the size of the weights of deep neural networks (DNNs).
Muhammad Hamis Haider, Seok-Bum Ko
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Energy-Efficient IoT with Deep Learning: Optimizing Resource Allocation in Smart Grids
2023 9th International Conference on Smart Structures and Systems (ICSSS), 2023The integration of Internet of Things (IoT) technology with deep reinforcement learning (DRL) has emerged as a transformative approach in the realm of smart grid management.
Rahul Mishra +5 more
semanticscholar +1 more source