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A Double Deep Q-Learning Model for Energy-Efficient Edge Scheduling
IEEE Transactions on Services Computing, 2019Reducing energy consumption is a vital and challenging problem for the edge computing devices since they are always energy-limited. To tackle this problem, a deep Q-learning model with multiple DVFS (dynamic voltage and frequency scaling) algorithms was proposed for energy-efficient scheduling (DQL-EES). However, DQL-EES is highly unstable when using a
Qingchen Zhang, Samee U Khan, Peng Li
exaly +3 more sources
Symposium on VLSI Circuits, 2019
An energy efficient deep neural network (DNN) learning processor is proposed using direct feedback alignment (DFA). The proposed processor achieves $2.2 \times$ faster learning speed compared with the previous learning processors by the pipelined DFA ...
Donghyeon Han +3 more
semanticscholar +1 more source
An energy efficient deep neural network (DNN) learning processor is proposed using direct feedback alignment (DFA). The proposed processor achieves $2.2 \times$ faster learning speed compared with the previous learning processors by the pipelined DFA ...
Donghyeon Han +3 more
semanticscholar +1 more source
Energy-Efficient Processors for Deep Learning Transcript
20192017 ISSCC Short Courses and ...
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Deep learning for energy-efficient beyond 5G networks
2020Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements.
Zappone, Alessio +2 more
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Deep learning innovations for energy efficiency
Το παγκόσμιο ενεργειακό τοπίο υφίσταται μια βαθιά μεταμόρφωση, γνωστή και ως ενεργειακή μετάβαση, η οποία καθοδηγείται από την επείγουσα ανάγκη για μετριασμό της κλιματικής αλλαγής, τη μείωση των εκπομπών αερίων του θερμοκηπίου και τη διασφάλιση βιώσιμων ενεργειακών πόρων.openaire +1 more source
UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision
IEEE Journal of Solid-State Circuits, 2019An energy-efficient deep neural network (DNN) accelerator, unified neural processing unit (UNPU), is proposed for mobile deep learning applications. The UNPU can support both convolutional layers (CLs) and recurrent or fully connected layers (FCLs) to ...
Jin-Su Lee +5 more
semanticscholar +1 more source
Energy Efficient Deep Learning Inference Embedded on FPGA for Sleep Apnea Detection
Journal of Signal Processing Systems, 2022Omiya Hassan +7 more
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HIGH PERFORMANCE AND ENERGY EFFICIENT DEEP LEARNING MODELS
2022Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven data analytics. We propose ANN-SNN conversion using “soft re-set” spiking neuron model, referred to as Residual Membrane Potential (RMP) spiking neuron, which retains the ...
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Toward Energy-Efficient Spike-Based Deep Reinforcement Learning With Temporal Coding
IEEE Computational Intelligence MagazineDeep reinforcement learning (DRL) facilitates efficient interaction with complex environments by enabling continuous optimization strategies and providing agents with autonomous learning abilities.
Ma-Lu Zhang +8 more
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