Computational memory-based inference and training of deep neural networks

A. Sebastian; I. Boybat; M. Dazzi; I. Giannopoulos; V. Jonnalagadda; V. Joshi; G. Karunaratne; B. Kersting; R. Khaddam-Aljameh; S. R. Nandakumar; A. Petropoulos; C. Piveteau; T. Antonakopoulos; B. Rajendran; M. Le Gallo; E. Eleftheriou

doi:10.23919/VLSIC.2019.8778178

Computational memory-based inference and training of deep neural networks

A. Sebastian, I. Boybat, M. Dazzi, I. Giannopoulos, V. Jonnalagadda, V. Joshi, G. Karunaratne, B. Kersting, R. Khaddam-Aljameh, S. R. Nandakumar, A. Petropoulos, C. Piveteau, T. Antonakopoulos, B. Rajendran, M. Le Gallo, E. Eleftheriou

Research output: Contribution to journal › Conference paper › peer-review

25 Citations (Scopus)

Abstract

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.

Original language	English
Pages (from-to)	T168-T169
Journal	Digest of Technical Papers - Symposium on VLSI Technology
DOIs	https://doi.org/10.23919/VLSIC.2019.8778178 https://doi.org/10.23919/VLSIT.2019.8776518
Publication status	Published - Jun 2019
Event	39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japan Duration: 9 Jun 2019 → 14 Jun 2019

Keywords

deep learning
In-memory computing
PCM

Access to Document

Cite this

Sebastian, A., Boybat, I., Dazzi, M., Giannopoulos, I., Jonnalagadda, V., Joshi, V., Karunaratne, G., Kersting, B., Khaddam-Aljameh, R., Nandakumar, S. R., Petropoulos, A., Piveteau, C., Antonakopoulos, T., Rajendran, B., Gallo, M. L., & Eleftheriou, E. (2019). Computational memory-based inference and training of deep neural networks. Digest of Technical Papers - Symposium on VLSI Technology, T168-T169. https://doi.org/10.23919/VLSIC.2019.8778178, https://doi.org/10.23919/VLSIT.2019.8776518

@article{1bce2b67e65d4c16a22a63e82f5b18e1,

title = "Computational memory-based inference and training of deep neural networks",

abstract = "In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.",

keywords = "deep learning, In-memory computing, PCM",

author = "A. Sebastian and I. Boybat and M. Dazzi and I. Giannopoulos and V. Jonnalagadda and V. Joshi and G. Karunaratne and B. Kersting and R. Khaddam-Aljameh and Nandakumar, {S. R.} and A. Petropoulos and C. Piveteau and T. Antonakopoulos and B. Rajendran and Gallo, {M. Le} and E. Eleftheriou",

year = "2019",

month = jun,

doi = "10.23919/VLSIC.2019.8778178",

language = "English",

pages = "T168--T169",

journal = "Digest of Technical Papers - Symposium on VLSI Technology",

issn = "0743-1562",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "39th Symposium on VLSI Technology, VLSI Technology 2019 ; Conference date: 09-06-2019 Through 14-06-2019",

}

Sebastian, A, Boybat, I, Dazzi, M, Giannopoulos, I, Jonnalagadda, V, Joshi, V, Karunaratne, G, Kersting, B, Khaddam-Aljameh, R, Nandakumar, SR, Petropoulos, A, Piveteau, C, Antonakopoulos, T, Rajendran, B, Gallo, ML & Eleftheriou, E 2019, 'Computational memory-based inference and training of deep neural networks', Digest of Technical Papers - Symposium on VLSI Technology, pp. T168-T169. https://doi.org/10.23919/VLSIC.2019.8778178, https://doi.org/10.23919/VLSIT.2019.8776518

TY - JOUR

T1 - Computational memory-based inference and training of deep neural networks

AU - Sebastian, A.

AU - Boybat, I.

AU - Dazzi, M.

AU - Giannopoulos, I.

AU - Jonnalagadda, V.

AU - Joshi, V.

AU - Karunaratne, G.

AU - Kersting, B.

AU - Khaddam-Aljameh, R.

AU - Nandakumar, S. R.

AU - Petropoulos, A.

AU - Piveteau, C.

AU - Antonakopoulos, T.

AU - Rajendran, B.

AU - Gallo, M. Le

AU - Eleftheriou, E.

PY - 2019/6

Y1 - 2019/6

N2 - In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.

AB - In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.

KW - deep learning

KW - In-memory computing

KW - PCM

UR - http://www.scopus.com/inward/record.url?scp=85070279182&partnerID=8YFLogxK

U2 - 10.23919/VLSIC.2019.8778178

DO - 10.23919/VLSIC.2019.8778178

M3 - Conference paper

AN - SCOPUS:85070279182

SN - 0743-1562

SP - T168-T169

JO - Digest of Technical Papers - Symposium on VLSI Technology

JF - Digest of Technical Papers - Symposium on VLSI Technology

T2 - 39th Symposium on VLSI Technology, VLSI Technology 2019

Y2 - 9 June 2019 through 14 June 2019

ER -

Computational memory-based inference and training of deep neural networks

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this