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RRAM-Based Analog Approximate Computing.
- Source :
-
IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems . Dec2015, Vol. 34 Issue 12, p1905-1917. 13p. - Publication Year :
- 2015
-
Abstract
- Approximate computing is a promising design paradigm for better performance and power efficiency. In this paper, we propose a power efficient framework for analog approximate computing with the emerging metal-oxide resistive switching random-access memory (RRAM) devices. A programmable RRAM-based approximate computing unit (RRAM-ACU ) is introduced first to accelerate approximated computation, and an approximate computing framework with scalability is then proposed on top of the RRAM-ACU. In order to program the RRAM-ACU efficiently, we also present a detailed configuration flow, which includes a customized approximator training scheme, an approximator-parameter-to-RRAM-state mapping algorithm, and an RRAM state tuning scheme. Finally, the proposed RRAM-based computing framework is modeled at system level. A predictive compact model is developed to estimate the configuration overhead of RRAM-ACU and help explore the application scenarios of RRAM-based analog approximate computing. The simulation results on a set of diverse benchmarks demonstrate that, compared with a x86–64 CPU at 2 GHz, the RRAM-ACU is able to achieve 4.06– 196.41 \times speedup and power efficiency of 24.59–567.98 GFLOPS/W with quality loss of 8.72% on average. And the implementation of hierarchical model and X application demonstrates that the proposed RRAM-based approximate computing framework can achieve >12.8 $\times$ power efficiency than its pure digital implementation counterparts (CPU, graphics processing unit, and field- programmable gate arrays). [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 02780070
- Volume :
- 34
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems
- Publication Type :
- Academic Journal
- Accession number :
- 111152837
- Full Text :
- https://doi.org/10.1109/TCAD.2015.2445741