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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

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 (US)
Title of host publication	2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	T168-T169
ISBN (Electronic)	9784863487185
DOIs	https://doi.org/10.23919/VLSIC.2019.8778178
State	Published - Jun 2019
Event	33rd Symposium on VLSI Circuits, VLSI Circuits 2019 - Kyoto, Japan Duration: Jun 9 2019 → Jun 14 2019

Publication series

Name	IEEE Symposium on VLSI Circuits, Digest of Technical Papers
Volume	2019-June

Conference

Conference	33rd Symposium on VLSI Circuits, VLSI Circuits 2019
Country/Territory	Japan
City	Kyoto
Period	6/9/19 → 6/14/19

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Keywords

In-memory computing
PCM
deep learning

Access to Document

10.23919/VLSIC.2019.8778178

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. In 2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers (pp. T168-T169). Article 8778178 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/VLSIC.2019.8778178

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

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",

note = "Publisher Copyright: {\textcopyright} 2019 JSAP.; 33rd Symposium on VLSI Circuits, VLSI Circuits 2019 ; Conference date: 09-06-2019 Through 14-06-2019",

year = "2019",

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doi = "10.23919/VLSIC.2019.8778178",

language = "English (US)",

series = "IEEE Symposium on VLSI Circuits, Digest of Technical Papers",

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

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booktitle = "2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers",

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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. in 2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers., 8778178, IEEE Symposium on VLSI Circuits, Digest of Technical Papers, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. T168-T169, 33rd Symposium on VLSI Circuits, VLSI Circuits 2019, Kyoto, Japan, 6/9/19. https://doi.org/10.23919/VLSIC.2019.8778178

Computational memory-based inference and training of deep neural networks. / Sebastian, A.; Boybat, I.; Dazzi, M. et al.
2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc., 2019. p. T168-T169 8778178 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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.

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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 - In-memory computing

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BT - 2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers

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T2 - 33rd Symposium on VLSI Circuits, VLSI Circuits 2019

Y2 - 9 June 2019 through 14 June 2019

ER -

Sebastian A, Boybat I, Dazzi M, Giannopoulos I, Jonnalagadda V, Joshi V et al. Computational memory-based inference and training of deep neural networks. In 2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc. 2019. p. T168-T169. 8778178. (IEEE Symposium on VLSI Circuits, Digest of Technical Papers). doi: 10.23919/VLSIC.2019.8778178

Computational memory-based inference and training of deep neural networks

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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