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19 results on '"Huang, Hantao"'

11. A Highly Parallel and Energy Efficient Three-Dimensional Multilayer CMOS-RRAM Accelerator for Tensorized Neural Network.

Author

Huang, Hantao, Ni, Leibin, Wang, Kanwen, Wang, Yuangang, and Yu, Hao

Abstract

It is a grand challenge to develop highly parallel yet energy-efficient machine learning hardware accelerator. This paper introduces a three-dimensional (3-D) multilayer CMOS-RRAM accelerator for a tensorized neural network. Highly parallel matrix–vector multiplication can be performed with low power in the proposed 3-D multilayer CMOS-RRAM accelerator. The adoption of tensorization can significantly compress the weight matrix of a neural network using much fewer parameters. Simulation results using the benchmark MNIST show that the proposed accelerator has ${\text{1.283}} \times$ speed-up, ${\text{4.276}} \times$ energy-saving, and ${\text{9.339}} \times$ area-saving compared to the 3-D CMOS-ASIC implementation; and ${\text{6.37}}\times$ speed-up and ${\text{2612}} \times$ energy-saving compared to 2-D CPU implementation. In addition, ${\text{14.85}}\times$ model compression can be achieved by tensorization with acceptable accuracy loss. [ABSTRACT FROM AUTHOR]

Published
2018
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12. LTNN: A Layerwise Tensorized Compression of Multilayer Neural Network.

Author

Huang, Hantao and Yu, Hao

Subjects
*ARTIFICIAL neural networks, *DEEP learning, *DATA compression
Abstract

An efficient deep learning requires a memory-efficient construction of a neural network. This paper introduces a layerwise tensorized formulation of a multilayer neural network, called LTNN, such that the weight matrix can be significantly compressed during training. By reshaping the multilayer neural network weight matrix into a high-dimensional tensor with a low-rank approximation, significant network compression can be achieved with maintained accuracy. An according layerwise training is developed by a modified alternating least-squares method with backward propagation for fine-tuning only. LTNN can provide the state-of-the-art results on various benchmarks with significant compression. For MNIST benchmark, LTNN shows $64 \times $ compression rate without accuracy drop. For Imagenet12 benchmark, our proposed LTNN achieves $35.84 \times $ compression of the neural network with around 2% accuracy drop. We have also shown $1.615 \times $ faster on inference speed than the existing works due to the smaller tensor core ranks. [ABSTRACT FROM AUTHOR]

Published
2019
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15. A Multiagent Minority-Game-Based Demand-Response Management of Smart Buildings Toward Peak Load Reduction.

Author

Huang, Hantao, Cai, Yuehua, Xu, Hang, and Yu, Hao

Subjects
*INTELLIGENT buildings, *MULTIAGENT systems, *GAME theory, *PEAK load, *CYBER physical systems, *ENERGY management
Abstract

This paper presents a cyber-physical management of smart buildings based on smart-gateway network with distributed and real-time energy data collection and analytics. We consider a building with multiple rooms supplied with one main electricity grid and one additional solar energy grid. Based on smart-gateway network, energy signatures of rooms are first extracted with consideration of uncertainty and further classified as different types of agents. Then, a multiagent minority-game (MG)-based demand-response management is introduced to reduce peak demand on the main electricity grid and also to fairly allocate solar energy on the additional grid. Experiment results show that compared to the traditional static and centralized energy-management system (EMS), and the recent multiagent EMS using price-demand competition, the proposed uncertainty-aware MG-EMS can achieve up to $50\times $ and $145\times $ utilization rate improvements, respectively, regarding to the fairness of solar energy resource allocation. More importantly, the peak load from the main electricity grid is reduced by 38.50% in summer and 15.83% in winter based on benchmarked energy data of building. Lastly, an average 23% uncertainty can be reduced with an according 37% balanced energy allocation improved comparing to the MG-EMS without consideration of uncertainty. [ABSTRACT FROM PUBLISHER]

Published
2017
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18. Fair energy resource allocation by minority game algorithm for smart buildings.

Author

Zhang, Chun, Wu, Wei, Huang, Hantao, and Yu, Hao

Abstract

Real-time and decentralized energy resource allocation has become the main feature to develop for the next generation energy management system (EMS). In this paper, a minority game (MG)-based EMS (MG-EMS) is proposed for smart buildings with hybrid energy sources: main energy resource from electrical power-grid and renewable energy resource from solar photovoltaic (PV) cells. Compared to the traditional static and centralized EMS (SC-EMS), and the recent multi-agent-based EMS (MA-EMS) based on price-demand competition, our proposed MG-EMS can achieve up to 51x and 147x utilization rate improvements respectively regarding to the fairness of solar energy resource allocation. In addition, the proposed MG-EMS can also reduce peak energy demand for main power-grid by 30.6%. As such, one can significantly reduce the cost and improve the stability of micro-grid of smart buildings with a high utilization rate of solar energy. [ABSTRACT FROM PUBLISHER]

Published
2012
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19. A real-time cyber-physical energy management system for smart houses.

Author

Wu, Wei, Aziz, Muhammad Khalid, Huang, Hantao, Yu, Hao, and Gooi, Hoay Beng

Abstract

The substitution of non-renewable energy by renewable energy as electricity supply is an emerging trend for development of smart houses nowadays. For example, solar photovoltaic (PV) panels, rechargeable batteries, external power supply, and power-consuming houses have formed a distributed micro-smart-grid. The traditional energy management system (EMS) for such a micro-smart-grid is, however, based on static demand model. As such, the resource (solar energy) cannot be optimally allocated to each house with real-time adjustment when the battery profile is different. In order to achieve high utilization rate of solar-energy, we propose a cyber-physical controller for real-time EMS of smart houses in this paper. Based on physically sensed battery profile, one cyber-physical controller enables the resource to be shared between different houses. The micro-smart-grid is modeled by decentralized multi-supplier and multi-customer system. The proposed real-time EMS is formulated based on the modified minority-game (MG) algorithm such that the task (or EU load) can be allocated with balanced distribution and hence improved utilization rate. The experiment results show that the proposed EMS can increase the solar energy utilization rate by 12.78% on average (up to 20%) when compared to the traditional design under the static demand model. Moreover, the load balancing of tasks (or customers) on each battery (or supplier) is significantly improved. For example, the average standard deviation of the load of tasks on batteries in each month is reduced from 45.36 KWh (static demand model) to 5.19 KWh (proposed model). The improved load balancing can further prolong lifetime of batteries. [ABSTRACT FROM PUBLISHER]

Published
2011
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