Energy efficient in-memory computing platform based on 4-terminal spin hall effect-driven domain wall motion devices

Shaahin Angizi; Zhezhi He; Deliang Fan

doi:10.1145/3060403.3060459

Energy efficient in-memory computing platform based on 4-terminal spin hall effect-driven domain wall motion devices

Shaahin Angizi, Zhezhi He, Deliang Fan

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

12 Scopus citations

Abstract

In this paper, we propose an energy efficient in-memory computing platform based on novel 4-terminal spin Hall effect-driven domain wall motion devices that could be employed as both non-volatile memory cell and in-memory logic unit. The proposed designs lead to unity of memory and logic. The device to architecture level simulation results show that, with 45% area increase, the proposed in-memory computing platform achieves the write energy ∼ 15.6 fJ/bit which is more than one order lower than that of standard 1-transistor 1-magnetic tunnel junction counterpart while keeping the identical 1ns writing speed. In addition, the proposed in-memory logic scheme improves the operating energy by 61.3% as compared with the conventional nonvolatile in-memory logic designs.

Original language	English (US)
Title of host publication	GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017
Publisher	Association for Computing Machinery
Pages	77-82
Number of pages	6
ISBN (Electronic)	9781450349727
DOIs	https://doi.org/10.1145/3060403.3060459
State	Published - May 10 2017
Externally published	Yes
Event	27th Great Lakes Symposium on VLSI, GLSVLSI 2017 - Banff, Canada Duration: May 10 2017 → May 12 2017

Publication series

Name	Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
Volume	Part F127756

Conference

Conference	27th Great Lakes Symposium on VLSI, GLSVLSI 2017
Country/Territory	Canada
City	Banff
Period	5/10/17 → 5/12/17

All Science Journal Classification (ASJC) codes

General Engineering

Keywords

Domain wall motion device
In-memory computing
Spin Hall effect

Access to Document

10.1145/3060403.3060459

Cite this

Angizi, S., He, Z., & Fan, D. (2017). Energy efficient in-memory computing platform based on 4-terminal spin hall effect-driven domain wall motion devices. In GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017 (pp. 77-82). (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI; Vol. Part F127756). Association for Computing Machinery. https://doi.org/10.1145/3060403.3060459

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title = "Energy efficient in-memory computing platform based on 4-terminal spin hall effect-driven domain wall motion devices",

abstract = "In this paper, we propose an energy efficient in-memory computing platform based on novel 4-terminal spin Hall effect-driven domain wall motion devices that could be employed as both non-volatile memory cell and in-memory logic unit. The proposed designs lead to unity of memory and logic. The device to architecture level simulation results show that, with 45% area increase, the proposed in-memory computing platform achieves the write energy ∼ 15.6 fJ/bit which is more than one order lower than that of standard 1-transistor 1-magnetic tunnel junction counterpart while keeping the identical 1ns writing speed. In addition, the proposed in-memory logic scheme improves the operating energy by 61.3% as compared with the conventional nonvolatile in-memory logic designs.",

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author = "Shaahin Angizi and Zhezhi He and Deliang Fan",

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Angizi, S, He, Z & Fan, D 2017, Energy efficient in-memory computing platform based on 4-terminal spin hall effect-driven domain wall motion devices. in GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017. Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, vol. Part F127756, Association for Computing Machinery, pp. 77-82, 27th Great Lakes Symposium on VLSI, GLSVLSI 2017, Banff, Canada, 5/10/17. https://doi.org/10.1145/3060403.3060459

Energy efficient in-memory computing platform based on 4-terminal spin hall effect-driven domain wall motion devices. / Angizi, Shaahin; He, Zhezhi; Fan, Deliang.
GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017. Association for Computing Machinery, 2017. p. 77-82 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI; Vol. Part F127756).

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

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N2 - In this paper, we propose an energy efficient in-memory computing platform based on novel 4-terminal spin Hall effect-driven domain wall motion devices that could be employed as both non-volatile memory cell and in-memory logic unit. The proposed designs lead to unity of memory and logic. The device to architecture level simulation results show that, with 45% area increase, the proposed in-memory computing platform achieves the write energy ∼ 15.6 fJ/bit which is more than one order lower than that of standard 1-transistor 1-magnetic tunnel junction counterpart while keeping the identical 1ns writing speed. In addition, the proposed in-memory logic scheme improves the operating energy by 61.3% as compared with the conventional nonvolatile in-memory logic designs.

AB - In this paper, we propose an energy efficient in-memory computing platform based on novel 4-terminal spin Hall effect-driven domain wall motion devices that could be employed as both non-volatile memory cell and in-memory logic unit. The proposed designs lead to unity of memory and logic. The device to architecture level simulation results show that, with 45% area increase, the proposed in-memory computing platform achieves the write energy ∼ 15.6 fJ/bit which is more than one order lower than that of standard 1-transistor 1-magnetic tunnel junction counterpart while keeping the identical 1ns writing speed. In addition, the proposed in-memory logic scheme improves the operating energy by 61.3% as compared with the conventional nonvolatile in-memory logic designs.

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KW - Spin Hall effect

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Energy efficient in-memory computing platform based on 4-terminal spin hall effect-driven domain wall motion devices

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All Science Journal Classification (ASJC) codes

Keywords

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