Exploiting Reconfigurable Vector Processing for Energy-Efficient Computation in 3D-Stacked Memories

de Lima JPC, Santos PC, de Moura RF, Alves MA, Beck AC, Carro L (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 11444 LNCS

Pages Range: 262-276

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Darmstadt, DEU

ISBN: 9783030172268

DOI: 10.1007/978-3-030-17227-5_19

Abstract

Although Processing-in-Memory (PIM) architectures have helped to reduce the effect of the memory wall, the logic placed inside 3D-memories still faces the large disparity between DRAM and CMOS logic operations. Thereby, for a broad range of emerging data-intensive applications, the Functional Units (FUs) are usually underutilized, especially when the application presents poor temporal-locality. As applications demand irregular processing requirements on the different parts of their execution, this behavior can be used to reconfigure energy-reduction techniques, either by scaling frequency or by power-gating functional units. In this paper, we present the application-dependable characteristics that enable dynamic usage of energy-reduction techniques without performance degradation for highly constrained PIM designs. The experimental results show that the exploration of a reconfiguration mechanism can improve PIM system energy efficiency by 5 $$\times $$ and also can effectively benefit both memory-intensive and compute-intensive applications.

Involved external institutions

Universidade Federal do Rio Grande do Sul (UFRGS) BR Brazil (BR) Universidade Federal do Paraná (UFPR) BR Brazil (BR)

How to cite

APA:

de Lima, J.P.C., Santos, P.C., de Moura, R.F., Alves, M.A., Beck, A.C., & Carro, L. (2019). Exploiting Reconfigurable Vector Processing for Energy-Efficient Computation in 3D-Stacked Memories. In Roger Woods, Pedro Diniz, Christian Hochberger, Andreas Koch, Brent Nelson (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 262-276). Darmstadt, DEU: Springer Verlag.

MLA:

de Lima, João Paulo C., et al. "Exploiting Reconfigurable Vector Processing for Energy-Efficient Computation in 3D-Stacked Memories." Proceedings of the 15th International Symposium on Applied Reconfigurable Computing, ARC 2019, Darmstadt, DEU Ed. Roger Woods, Pedro Diniz, Christian Hochberger, Andreas Koch, Brent Nelson, Springer Verlag, 2019. 262-276.

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