Multiplexer Optimization for Adders in Stochastic Computing

Sercan Aygun; M. Hassan Najafi; Lida Kouhalvandi; Ece Olcay Gunes

doi:10.1145/3611315.3633256

Multiplexer Optimization for Adders in Stochastic Computing

Sercan Aygun
, M. Hassan Najafi
, Lida Kouhalvandi
, Ece Olcay Gunes

Elektronik ve Hab.Müh.Bölümü

University of Louisiana at Lafayette
Dogus University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

This study presents an optimization algorithm for multiplexer (MUX)-based scaled addition for stochastic computing (SC). Accumulation operation can be performed in SC using a MUX unit. Cascaded structures of 2m-to-1 MUXs are used for the accumulation of multiple terms. Optimizing these designs holds significance in cases of accumulating a large number of inputs. The depth of the cascaded MUXs varies with m, affecting the hardware cost, delay, and accuracy. The proposed algorithm performs stage-wise optimization of m. Evaluation results show a lower hardware cost and a higher accuracy compared to the standard MUX-based SC addition using 2-to-1 MUXs for SC-based neural networks.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023
Yayınlayan	Association for Computing Machinery
ISBN (Elektronik)	9798400703256
DOI'lar	https://doi.org/10.1145/3611315.3633256
Yayın durumu	Yayınlandı - 18 Ara 2023
Etkinlik	18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023 - Dresden, Germany Süre: 18 Ara 2023 → 20 Ara 2023

Yayın serisi

Adı	ACM International Conference Proceeding Series

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023
Ülke/Bölge	Germany
Şehir	Dresden
Periyot	18/12/23 → 20/12/23

Bibliyografik not

Publisher Copyright:
© 2023 ACM.

Finansman

This work was supported in part by National Science Foundation (NSF) grant #2019511, the Louisiana Board of Regents Support Fund #LEQSF(2020-23)-RD-A-26, and generous gifts from Cisco, Xilinx, and Nvidia.

Finansörler	Finansör numarası
National Science Foundation	2019511
Louisiana Board of Regents	2020-23)-RD-A-26

Belgeye Erişim

10.1145/3611315.3633256

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@inproceedings{692467ef3adf4ce3b8390fbd15fd6710,

title = "Multiplexer Optimization for Adders in Stochastic Computing",

abstract = "This study presents an optimization algorithm for multiplexer (MUX)-based scaled addition for stochastic computing (SC). Accumulation operation can be performed in SC using a MUX unit. Cascaded structures of 2m-to-1 MUXs are used for the accumulation of multiple terms. Optimizing these designs holds significance in cases of accumulating a large number of inputs. The depth of the cascaded MUXs varies with m, affecting the hardware cost, delay, and accuracy. The proposed algorithm performs stage-wise optimization of m. Evaluation results show a lower hardware cost and a higher accuracy compared to the standard MUX-based SC addition using 2-to-1 MUXs for SC-based neural networks.",

keywords = "Multiplexer, neural networks, optimization, stochastic computing",

author = "Sercan Aygun and Najafi, \{M. Hassan\} and Lida Kouhalvandi and Gunes, \{Ece Olcay\}",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023 ; Conference date: 18-12-2023 Through 20-12-2023",

year = "2023",

month = dec,

day = "18",

doi = "10.1145/3611315.3633256",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

booktitle = "Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023",

}

Aygun, S, Najafi, MH, Kouhalvandi, L & Gunes, EO 2023, Multiplexer Optimization for Adders in Stochastic Computing. in Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023., 18, ACM International Conference Proceeding Series, Association for Computing Machinery, 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023, Dresden, Germany, 18/12/23. https://doi.org/10.1145/3611315.3633256

Multiplexer Optimization for Adders in Stochastic Computing. / Aygun, Sercan; Najafi, M. Hassan; Kouhalvandi, Lida et al.
Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023. Association for Computing Machinery, 2023. 18 (ACM International Conference Proceeding Series).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Multiplexer Optimization for Adders in Stochastic Computing

AU - Aygun, Sercan

AU - Najafi, M. Hassan

AU - Kouhalvandi, Lida

AU - Gunes, Ece Olcay

PY - 2023/12/18

Y1 - 2023/12/18

N2 - This study presents an optimization algorithm for multiplexer (MUX)-based scaled addition for stochastic computing (SC). Accumulation operation can be performed in SC using a MUX unit. Cascaded structures of 2m-to-1 MUXs are used for the accumulation of multiple terms. Optimizing these designs holds significance in cases of accumulating a large number of inputs. The depth of the cascaded MUXs varies with m, affecting the hardware cost, delay, and accuracy. The proposed algorithm performs stage-wise optimization of m. Evaluation results show a lower hardware cost and a higher accuracy compared to the standard MUX-based SC addition using 2-to-1 MUXs for SC-based neural networks.

AB - This study presents an optimization algorithm for multiplexer (MUX)-based scaled addition for stochastic computing (SC). Accumulation operation can be performed in SC using a MUX unit. Cascaded structures of 2m-to-1 MUXs are used for the accumulation of multiple terms. Optimizing these designs holds significance in cases of accumulating a large number of inputs. The depth of the cascaded MUXs varies with m, affecting the hardware cost, delay, and accuracy. The proposed algorithm performs stage-wise optimization of m. Evaluation results show a lower hardware cost and a higher accuracy compared to the standard MUX-based SC addition using 2-to-1 MUXs for SC-based neural networks.

KW - Multiplexer

KW - neural networks

KW - optimization

KW - stochastic computing

UR - https://www.scopus.com/pages/publications/85184301041

U2 - 10.1145/3611315.3633256

DO - 10.1145/3611315.3633256

M3 - Conference contribution

AN - SCOPUS:85184301041

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023

PB - Association for Computing Machinery

T2 - 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023

Y2 - 18 December 2023 through 20 December 2023

ER -

Multiplexer Optimization for Adders in Stochastic Computing

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

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