Memristors and memristor crossbar arrays have been widely studied for neuromorphic and other in-memory computing applications. To achieve optimal system performance, however, it is essential to integrate memristor crossbars with peripheral and control circuitry. Here, we report a fully functional, hybrid memristor chip in which a passive crossbar array is directly integrated with custom-designed circuits, including a full set of mixed-signal interface blocks and a digital processor for reprogrammable computing. The memristor crossbar array enables online learning and forward and backward vector-matrix operations, while the integrated interface and control circuitry allow mapping of different algorithms on chip. The system supports charge-domain operation to overcome the nonlinear I–V characteristics of memristor devices through pulse width modulation and custom analogue-to-digital converters. The integrated chip offers all the functions required for operational neuromorphic computing hardware. Accordingly, we demonstrate a perceptron network, sparse coding algorithm and principal component analysis with an integrated classification layer using the system.
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The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.
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The authors acknowledge inspiring discussions with C. Liu, T. Chou, P. Brown, M.A. Zidan and P.M. Sheridan. This work was supported in part by the Defense Advanced Research Projects Agency (DARPA) through award HR0011-13-2-0015, the National Science Foundation (NSF) through awards CCF-1617315 and 1734871, and the Applications Driving Architectures (ADA) Research Centre, a JUMP Centre co-sponsored by SRC and DARPA.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–30, Supplementary notes 1–11