Mixed-precision in-memory computing

Abstract

As complementary metal–oxide–semiconductor (CMOS) scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to extend the performance of today’s computers substantially. In-memory computing is a promising approach in which nanoscale resistive memory devices, organized in a computational memory unit, are used for both processing and memory. However, to reach the numerical accuracy typically required for data analytics and scientific computing, limitations arising from device variability and non-ideal device characteristics need to be addressed. Here we introduce the concept of mixed-precision in-memory computing, which combines a von Neumann machine with a computational memory unit. In this hybrid system, the computational memory unit performs the bulk of a computational task, while the von Neumann machine implements a backward method to iteratively improve the accuracy of the solution. The system therefore benefits from both the high precision of digital computing and the energy/areal efficiency of in-memory computing. We experimentally demonstrate the efficacy of the approach by accurately solving systems of linear equations, in particular, a system of 5,000 equations using 998,752 phase-change memory devices.

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Fig. 1: Concept of mixed-precision in-memory computing.
Fig. 2: Scalar multiplication.
Fig. 3: Solution of a system of linear equations involving a model covariance matrix.
Fig. 4: Estimation of autophagy-related gene interactions from RNA measurements.
Algorithm 1
Algorithm 2

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Acknowledgements

We thank C. Malossi, M. Rodriguez, C. Hagleitner, L. Kull and T. Toifl for discussions, N. Papandreou, A. Athmanathan and U. Egger for experimental help, T. Delbruck for reviewing the manuscript, and C. Bolliger for help with preparation of the manuscript. A.S. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 682675).

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M.L.G., A.S., T.T., C.B., A.C. and E.E. conceived the concept of mixed-precision in-memory computing. M.L.G., A.S. and C.B. designed the research. M.L.G. implemented the mixed-precision in-memory computing system and performed all experiments. M.L.G., R.M. and M.M. performed the research on the RNA expression data. H.G. performed the evaluation of the runtime and energy consumption. All authors contributed to the analysis and interpretation of the results. M.L.G. and A.S. co-wrote the manuscript based on the input from all authors.

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Correspondence to Manuel Le Gallo or Abu Sebastian.

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Le Gallo, M., Sebastian, A., Mathis, R. et al. Mixed-precision in-memory computing. Nat Electron 1, 246–253 (2018). https://doi.org/10.1038/s41928-018-0054-8

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