Abstract
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.
Original language | English (US) |
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Article number | 9273250 |
Pages (from-to) | 237-241 |
Number of pages | 5 |
Journal | IEEE Transactions on Very Large Scale Integration (VLSI) Systems |
Volume | 29 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
All Science Journal Classification (ASJC) codes
- Software
- Hardware and Architecture
- Electrical and Electronic Engineering