ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator
Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/12/7/838 |
| _version_ | 1797526566914228224 |
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| author | Dong Hyun Hwang Chang Yeop Han Hyun Woo Oh Seung Eun Lee |
| author_facet | Dong Hyun Hwang Chang Yeop Han Hyun Woo Oh Seung Eun Lee |
| author_sort | Dong Hyun Hwang |
| collection | DOAJ |
| description | Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end. |
| first_indexed | 2024-03-10T09:31:09Z |
| format | Article |
| id | doaj.art-116e35d5ec4b490da2ded86ac77bd12b |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T09:31:09Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-116e35d5ec4b490da2ded86ac77bd12b2023-11-22T04:25:18ZengMDPI AGMicromachines2072-666X2021-07-0112783810.3390/mi12070838ASimOV: A Framework for Simulation and Optimization of an Embedded AI AcceleratorDong Hyun Hwang0Chang Yeop Han1Hyun Woo Oh2Seung Eun Lee3Department of Electronic Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, KoreaDepartment of Electronic Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, KoreaDepartment of Electronic Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, KoreaDepartment of Electronic Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, KoreaArtificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.https://www.mdpi.com/2072-666X/12/7/838artificial intelligencek-NNembedded system |
| spellingShingle | Dong Hyun Hwang Chang Yeop Han Hyun Woo Oh Seung Eun Lee ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator Micromachines artificial intelligence k-NN embedded system |
| title | ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator |
| title_full | ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator |
| title_fullStr | ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator |
| title_full_unstemmed | ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator |
| title_short | ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator |
| title_sort | asimov a framework for simulation and optimization of an embedded ai accelerator |
| topic | artificial intelligence k-NN embedded system |
| url | https://www.mdpi.com/2072-666X/12/7/838 |
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