L773:2158 1525 OR L773:0271 4310 OR L773:9781467357609 OR L773:9781467357623
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An application specific vector processor for CNN-based massive MIMO positioning
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- Attari, Mohammad (author)
- Lund University,Lunds universitet,Institutionen för elektro- och informationsteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Integrerade elektroniksystem,Forskargrupper vid Lunds universitet,Department of Electrical and Information Technology,Departments at LTH,Faculty of Engineering, LTH,Integrated Electronic Systems,Lund University Research Groups
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- Sánchez, Jesús Rodríguez (author)
- Lund University,Lunds universitet,Institutionen för elektro- och informationsteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Integrerade elektroniksystem,Forskargrupper vid Lunds universitet,Kommunikationsteknologi,Department of Electrical and Information Technology,Departments at LTH,Faculty of Engineering, LTH,Integrated Electronic Systems,Lund University Research Groups,Communications Engineering
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- Liu, Liang (author)
- Lund University,Lunds universitet,Institutionen för elektro- och informationsteknik,Institutioner vid LTH,Lunds Tekniska Högskola,System på chips (master),Utbildningsprogram, LTH,Integrerade elektroniksystem,Forskargrupper vid Lunds universitet,Department of Electrical and Information Technology,Departments at LTH,Faculty of Engineering, LTH,Embedded Electronics Engineering (M.Sc.),Educational programmes, LTH,Faculty of Engineering, LTH,Integrated Electronic Systems,Lund University Research Groups
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- 2021
- 2021
- English.
- In: 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. - 0271-4310. - 9781728192017 ; 2021-May
- Conference paper (peer-reviewed)
Abstract
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- This paper sets out to create an implementation for fingerprint-based positioning using massive multiple-input multiple-output (MIMO) technology, by means of deep convolutional neural networks (CNN), and utilizing the wireless channel state information (CSI). Due to the sheer volume of computational requirements imposed by CNN processing, an accelerator-assisted design is well-suited to the task at hand. Consequently, an application specific instruction set processor (ASIP) is designed to combine flexibility with implementation efficiency. This ASIP is equipped with vector processing capabilities employing a single instruction multiple data (SIMD) scheme, and additionally has a very large instruction word (VLIW) architecture to further exploit instruction-level parallelism. A configurable 2D array of processing engines (PE) is integrated into the processor, in a tightly coupled manner, to accelerate the CNN operation. Synthesis results will be demonstrated using the GF-22 nm FD-SOI technology with a clock frequency of 555 MHz. The system can achieve a throughput of 271 positionings/s, with an average positioning error of 3.5 λ (40 cm) at a carrier frequency of 2.6 GHz.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
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