TY - JOUR
T1 - ReLOPE
T2 - Resistive RAM-Based Linear First-Order Partial Differential Equation Solver
AU - Ensan, Sina Sayyah
AU - Ghosh, Swaroop
N1 - Funding Information:
Manuscript received June 24, 2020; revised October 4, 2020; accepted October 28, 2020. Date of publication November 30, 2020; date of current version December 29, 2020. This work was supported by SRC under Grant 2847.001 and NSF under Grant CNS-1722557, Grant CCF-1718474, Grant DGE-1723687, and Grant DGE-1821766. (Corresponding author: Sina Sayyah Ensan.) The authors are with the School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802 USA (e-mail: sxs2541@psu.edu; szg212@psu.edu).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - Data movement between memory and processing units poses an energy barrier to Von-Neumann-based architectures. In-memory computing (IMC) eliminates this barrier. RRAM-based IMC has been explored for data-intensive applications, such as artificial neural networks and matrix-vector multiplications that are considered as 'soft' tasks where performance is a more important factor than accuracy. In 'hard' tasks such as partial differential equations (PDEs), accuracy is a determining factor. In this brief, we propose ReLOPE, a fully RRAM crossbar-based IMC to solve PDEs using the Runge-Kutta numerical method with 97% accuracy. ReLOPE expands the operating range of solution by exploiting shifters to shift input data and output data. ReLOPE range of operation and accuracy can be expanded by using fine-grained step sizes by programming other RRAMs on the BL. Compared to software-based PDE solvers, ReLOPE gains 31.4× energy reduction at only 3% accuracy loss.
AB - Data movement between memory and processing units poses an energy barrier to Von-Neumann-based architectures. In-memory computing (IMC) eliminates this barrier. RRAM-based IMC has been explored for data-intensive applications, such as artificial neural networks and matrix-vector multiplications that are considered as 'soft' tasks where performance is a more important factor than accuracy. In 'hard' tasks such as partial differential equations (PDEs), accuracy is a determining factor. In this brief, we propose ReLOPE, a fully RRAM crossbar-based IMC to solve PDEs using the Runge-Kutta numerical method with 97% accuracy. ReLOPE expands the operating range of solution by exploiting shifters to shift input data and output data. ReLOPE range of operation and accuracy can be expanded by using fine-grained step sizes by programming other RRAMs on the BL. Compared to software-based PDE solvers, ReLOPE gains 31.4× energy reduction at only 3% accuracy loss.
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U2 - 10.1109/TVLSI.2020.3035769
DO - 10.1109/TVLSI.2020.3035769
M3 - Article
AN - SCOPUS:85097432290
VL - 29
SP - 237
EP - 241
JO - IEEE Transactions on Very Large Scale Integration (VLSI) Systems
JF - IEEE Transactions on Very Large Scale Integration (VLSI) Systems
SN - 1063-8210
IS - 1
M1 - 9273250
ER -