Results 31 to 40 of about 16,838,211 (334)
Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes [PDF]
IEEE Transactions on Parallel and Distributed Systems, 2017 High-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-
E. Azarkhish +3 more
semanticscholar +1 more source
Biomimetics, 2023 With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era.
Chunlei Chen +7 more
doaj +1 more source
2-Bit-Per-Cell RRAM-Based In-Memory Computing for Area-/Energy-Efficient Deep Learning
IEEE Solid-State Circuits Letters, 2020 In-memory computing (IMC) has emerged as a promising technique for enhancing energy efficiency of deep neural networks (DNNs). While embedded nonvolatile memory, such as resistive RAM (RRAM) is a good alternative to SRAM/DRAM for IMC owing to high ...
Wangxin He +6 more
semanticscholar +1 more source
Deep Learning with Energy-efficient Binary Gradient Cameras
CoRR, 2016 Power consumption is a critical factor for the deployment of embedded computer vision systems. We explore the use of computational cameras that directly output binary gradient images to reduce the portion of the power consumption allocated to image sensing.
Suren Jayasuriya +3 more
openaire +2 more sources
Spiking Neural Network Discovers Energy-Efficient Hexapod Motion in Deep Reinforcement Learning
IEEE Access, 2021 In Deep Reinforcement Learning (DRL) for robotics application, it is important to find energy-efficient motions. For this purpose, a standard method is to set an action penalty in the reward to find the optimal motion considering the energy expenditure ...
Katsumi Naya +3 more
doaj +1 more source
Energy-efficient neural network learning with accuracy-adjustable floating-point multiplier
, 2023 This paper proposes a novel approximate bfloat16 multiplier with on-the-fly adjustable accuracy for energy-efficient learning in deep neural networks.
Ratko Pilipović (14498065) +2 more
core +1 more source
Deep Learning for Energy Efficiency in Smart Grids
American Journal of Artificial Intelligence and Neural Networks, 2022 Smart grids represent the future of energy distribution, offering enhanced efficiency, reliability, and sustainability. Deep learning techniques are playing a crucial role in optimizing energy consumption and distribution within these grids by enabling predictive modeling, fault detection, and demand forecasting. This article explores how deep learning
openaire +1 more source
Deep Reinforcement Learning for Energy-Efficient Networking with Reconfigurable Intelligent Surfaces [PDF]
ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020 When deployed as reflectors for existing wireless base stations (BSs), reconfigurable intelligent surfaces (RISs) can be a promising approach to achieve high spectrum and energy efficiency. However, due to the large number of RIS elements, the joint optimization of the BS and reflector RIS configuration is challenging. In essence, the BS transmit power
Gilsoo Lee +4 more
openaire +2 more sources
Deep Reinforcement Learning Based Energy Efficient Multi-UAV Data Collection for IoT Networks
IEEE Open Journal of Vehicular Technology, 2021 Unmanned aerial vehicles (UAVs) are regarded as an emerging technology, which can be effectively utilized to perform the data collection tasks in the Internet of Things (IoT) networks.
Seyed Saeed Khodaparast +3 more
doaj +1 more source
Application of Deep Learning for Energy Management in Smart Grid
, 2021 In the modern electronic power system, energy management and load forecasting are important tasks. Energy management systems are designed concerning monitoring and optimizing the energy requirement in smart systems. This research work is divided into two
Soumitra Ghosh +5 more
core +1 more source