Memristor Emulators for an Adaptive DPE Algorithm: Comparative Study

Vector Matrix Multiplication (VMM) is a complex operation requiring large computational power to fulfill one iteration. Resistive computing; including memristors, is one solution to speed up VMM by optimizing the multiplication process into few steps despite the matrices’ sizes. In this paper, we propose an Adaptive Dot Product Engine (ADPE) algorithm based on memristors for enhancing the process of resistive computing in VMM. The algorithm showed 5% error on preliminary results with one on-line training step for one layered crossbar array circuit of memristors. However memristors require new fabrication technologies where the design and validation processes of systems using these devices remains challenging. A comparison of various available circuits emulating a memristor suitable for ADPE is presented and models were compared based on chip size, circuit elements used and operating frequency.

[1]  Catherine E. Graves,et al.  Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine , 2018, Advanced materials.

[2]  Yunus Babacan,et al.  A new DDCC based memristor emulator circuit and its applications , 2014, Microelectron. J..

[3]  Stephen J. Wolf,et al.  The elusive memristor: properties of basic electrical circuits , 2008, 0807.3994.

[4]  Ugur Cam,et al.  New memristor emulator circuit using OTAs and CCIIs , 2015, 2015 9th International Conference on Electrical and Electronics Engineering (ELECO).

[5]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[6]  Leon O. Chua,et al.  Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors , 2009, Proceedings of the IEEE.

[7]  Vishal Saxena,et al.  A Compact CMOS Memristor Emulator Circuit and its Applications , 2017, 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS).

[8]  C. Toumazou,et al.  A Versatile Memristor Model With Nonlinear Dopant Kinetics , 2011, IEEE Transactions on Electron Devices.

[9]  Fırat Kaçar,et al.  Floating memristor emulator with subthreshold region , 2017 .

[10]  Dalibor Biolek,et al.  Behavioral Modeling of Memcapacitor , 2011 .

[11]  Uri C. Weiser,et al.  TEAM: ThrEshold Adaptive Memristor Model , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  Muhammad Taher Abuelma'atti,et al.  A continuous-level memristor emulator and its application in a multivibrator circuit , 2015 .

[13]  Carlos Sánchez-López,et al.  A 16 Hz–160 kHz memristor emulator circuit , 2015 .

[14]  Hirofumi Tanaka,et al.  Recent progress on fabrication of memristor and transistor-based neuromorphic devices for high signal processing speed with low power consumption , 2018 .

[15]  Yiran Chen,et al.  Emerging non-volatile memories: Opportunities and challenges , 2011, 2011 Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[16]  Dalibor Biolek,et al.  SPICE Model of Memristor with Nonlinear Dopant Drift , 2009 .

[17]  J. Simmons.  Generalized Formula for the Electric Tunnel Effect between Similar Electrodes Separated by a Thin Insulating Film , 1963 .

[18]  L. Chua.  Memristor-The missing circuit element , 1971 .

[19]  T. A. Wey,et al.  Amplitude modulator circuit featuring TiO2 memristor with linear dopant drift , 2009 .

[20]  K. Eshraghian,et al.  The fourth element: characteristics, modelling and electromagnetic theory of the memristor , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  R. Swarnalatha,et al.  Review and simulation of memristors using MATLAB and PSpice , 2017, 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[22]  E. Lehtonen,et al.  CNN using memristors for neighborhood connections , 2010, 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010).