Journal
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
Volume 34, Issue 12, Pages 1905-1917Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCAD.2015.2445741
Keywords
Approximate computing; neural network; power efficiency; resistive random-access memory (RRAM)
Categories
Funding
- 973 Project [2013CB329000]
- National Natural Science Foundation of China [61373026, 61261160501]
- Brain Inspired Computing Research, Tsinghua University [20141080934]
- Tsinghua University Initiative Scientific Research Program
- Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions, National Science Foundation [CNS-1253424, ECCS-1202225]
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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 4.06-196.41x 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 >12.8x power efficiency than its pure digital implementation counterparts (CPU, graphics processing unit, and field-programmable gate arrays).
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