Pub Date : 2023-05-18DOI: 10.1109/eIT57321.2023.10187292
Md Shahin Alam, K. R. Khan, Il-Seop Shin
Distributed energy resources, especially renewable energy in a building microgrid, are examined to improve the microgrid's performance. Proper management of the building microgrid through scheduling the energy resources is essential to maximize the benefit of implementing such a microgrid. This paper discusses innovative algorithms to manage energy flow from the different resources to improve the performance in terms of losses, operating costs, and emissions. A Particle Swarm Optimization method is applied to scheduling of the energy resources. Different case studies have been conducted to present the $mathbf{b}$ enefits of building microgrids' scheduling and to validate the proposed methodology. The results are discussed and compared to the experimental results, obtained from a building microgrid in a university campus. A sensitivity analysis is performed to see how the load and price uncertainty impact building microgrid operations. This research shows that integrating more renewables into the building microgrid and optimizing the scheduling help improve the performance during a 24-hour operation.
{"title":"Optimal Distributed Generator Scheduling in a Campus Microgrid - Case Study at a Building Microgrid","authors":"Md Shahin Alam, K. R. Khan, Il-Seop Shin","doi":"10.1109/eIT57321.2023.10187292","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187292","url":null,"abstract":"Distributed energy resources, especially renewable energy in a building microgrid, are examined to improve the microgrid's performance. Proper management of the building microgrid through scheduling the energy resources is essential to maximize the benefit of implementing such a microgrid. This paper discusses innovative algorithms to manage energy flow from the different resources to improve the performance in terms of losses, operating costs, and emissions. A Particle Swarm Optimization method is applied to scheduling of the energy resources. Different case studies have been conducted to present the $mathbf{b}$ enefits of building microgrids' scheduling and to validate the proposed methodology. The results are discussed and compared to the experimental results, obtained from a building microgrid in a university campus. A sensitivity analysis is performed to see how the load and price uncertainty impact building microgrid operations. This research shows that integrating more renewables into the building microgrid and optimizing the scheduling help improve the performance during a 24-hour operation.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121370861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.1109/eIT57321.2023.10187228
Hans Johnson, Tianyang Fang, Alejandro Perez-Vicente, J. Saniie
We propose a distributed system based on low-power embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best performance regarding latency and power efficiency. Our cluster was modular throughout the experiment, and we have implementations that consist of up to 12 Zynq-7020 chip-based boards as well as 5 UltraScale+ MPSoC FPGA boards connected through an ethernet switch, and the cluster will evaluate configurable Deep Learning Accelerator (DLA) Versatile Tensor Accelerator (VTA). This adaptable distributed architecture is distinguished by its capacity to evaluate and manage neural network workloads in numerous configurations which enables users to conduct multiple experiments tailored to their specific application needs. The proposed system can simultaneously execute diverse Neural Network (NN) models, arrange the computation graph in a pipeline structure, and manually allocate greater resources to the most computationally intensive layers of the NN graph.
{"title":"Reconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators","authors":"Hans Johnson, Tianyang Fang, Alejandro Perez-Vicente, J. Saniie","doi":"10.1109/eIT57321.2023.10187228","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187228","url":null,"abstract":"We propose a distributed system based on low-power embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best performance regarding latency and power efficiency. Our cluster was modular throughout the experiment, and we have implementations that consist of up to 12 Zynq-7020 chip-based boards as well as 5 UltraScale+ MPSoC FPGA boards connected through an ethernet switch, and the cluster will evaluate configurable Deep Learning Accelerator (DLA) Versatile Tensor Accelerator (VTA). This adaptable distributed architecture is distinguished by its capacity to evaluate and manage neural network workloads in numerous configurations which enables users to conduct multiple experiments tailored to their specific application needs. The proposed system can simultaneously execute diverse Neural Network (NN) models, arrange the computation graph in a pipeline structure, and manually allocate greater resources to the most computationally intensive layers of the NN graph.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126819728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/eIT57321.2023.10187252
Hans Johnson, Silvia Zorzetti, J. Saniie
This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.
{"title":"Exploration of Optimizing FPGA-based Qubit Controller for Experiments on Superconducting Quantum Computing Hardware","authors":"Hans Johnson, Silvia Zorzetti, J. Saniie","doi":"10.1109/eIT57321.2023.10187252","DOIUrl":"https://doi.org/10.1109/eIT57321.2023.10187252","url":null,"abstract":"This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115669169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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