Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle [PDF]
CoRR, 2023 Deep Learning has enabled many advances in machine learning applications in the last few years. However, since current Deep Learning algorithms require much energy for computations, there are growing concerns about the associated environmental costs ...
Vanessa Mehlin +2 more
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All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning [PDF]
Nanophotonics, 2022 In recent years, the computational demands of deep learning applications have necessitated the introduction of energy-efficient hardware accelerators. Optical neural networks are a promising option; however, thus far they have been largely limited by the
Li Gordon H.Y. +6 more
doaj +3 more sources
Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices. [PDF]
Sensors (Basel), 2023 Tiny machine learning (TinyML) has become an emerging field according to the rapid growth in the area of the internet of things (IoT). However, most deep learning algorithms are too complex, require a lot of memory to store data, and consume an enormous ...
Kim K, Jang SJ, Park J, Lee E, Lee SS.
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Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks. [PDF]
Sci Rep, 2023 Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in ...
Abunahla H +7 more
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Renewable-Aware Frequency Scaling Approach for Energy-Efficient Deep Learning Clusters
Applied SciencesRecently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters.
Hyuk-Gyu Park, Dong-Ki Kang
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Energy-Efficient Dynamic Workflow Scheduling in Cloud Environments Using Deep Learning
SensorsDynamic workflow scheduling in cloud environments is a challenging task due to task dependencies, fluctuating workloads, resource variability, and the need to balance makespan and energy consumption.
Sunera Chandrasiri, Dulani Meedeniya
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A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing
IEEE Access, 2019 Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However,
Zaiwar Ali +5 more
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ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing [PDF]
2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023 Deploying Deep Learning (DL) on embedded end devices is a scorching trend in pervasive computing. Since most Microcontrollers (MCUs) on embedded devices have limited computing power, it is necessary to add a DL accelerator.
Chao Qian +2 more
semanticscholar +1 more source
LiteCON: An All-photonic Neuromorphic Accelerator for Energy-efficient Deep Learning [PDF]
ACM Transactions on Architecture and Code Optimization (TACO), 2022 Deep learning is highly pervasive in today's data-intensive era. In particular, convolutional neural networks (CNNs) are being widely adopted in a variety of fields for superior accuracy.
Dharanidhar Dang, Bill Lin, D. Sahoo
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Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing
Communications of the ACM, 2023 support simulation of a neuromorphic chip with up to around 20,000 neural cores that was tested to run on 512 Nvidia A100 GPUs. In addition to the simulator, an FPGA-based hardware emulator for the neuro-morphic chip was also developed to accelerate the ...
Tao Luo +7 more
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