A novel, flexible circuit used to implement selected mathematical operations for AI algorithms optimized for hardware applications. (English) Zbl 07711011
Summary: The paper presents a novel circuit that enables an implementation of various mathematical operations with automatic conversion of the resulting multi-bit digital signals into voltage. The circuit consists of a number of parallel branches consisting of a combination of switches and resistors. Depending on the value of the resistive elements used, it enables the implementation of such operations as adding, subtracting and multiplying numbers stored as digital signals. Additionally, it can be used to obtain an approximation of selected nonlinear functions that include logarithmic, exponential, tangent functions. For binary weighted resistances \((1, 2, 4, 8, \ldots)\) in subsequent branches, the circuit works as a conventional voltage digital-to-analog converter. The functions that can be implemented with this circuit enable its application in various AI algorithms implemented as specialized integrated circuits. In this work, we focus in particular on its application to the implementation of basic operators of fuzzy logic systems. The circuit was implemented in CMOS 0.13 \(\mu\)m technology.
MSC:
| 68-XX | Computer science |
| 00Axx | General and miscellaneous specific topics |
| 68Txx | Artificial intelligence |
Keywords:
hardware realization of mathematical operations; fuzzy logic functions and operators; artificial intelligence; mixed analog and digital signals processing; digital-to-analog conversionReferences:
| [1] | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J., Learning Internal Representations By Error Propagation in: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, 318-362 (1986), MIT Press Cambridge |
| [2] | Haykin, S., Neural Network: A Comprehensive Foundation (1998), Prentice-Hall: Prentice-Hall Englewood Cliffs |
| [3] | Kohonen, T., Self-Organizing Maps (2001), Springer · Zbl 0957.68097 |
| [4] | Banach, M.; Długosz, R.; Talaśka, T.; Pedrycz, W., Air pollution monitoring system with prediction abilities based on smart autonomous sensors equipped with ANNs with novel training scheme, Remote Sens., 14, 2, 413 (2022) |
| [5] | Gatet, L.; Tap-Bteille, H.; Bony, F., Comparison between analog and digital neural network implementations for range-finding applications, IEEE Trans. Neural Netw., 20, 3, 460-470 (2009) |
| [6] | Oike, Y.; Ikeda, M.; Asada, K., A high-speed and low-voltage associative co-processor with exact Hamming/Manhattan-distance estimation using wordparallel and hierarchical search architecture, IEEE J. Solid-State Circuits, 39, 8, 1383-1387 (2004) |
| [7] | Talaśka, T.; Kolasa, M.; Długosz, R.; Pedrycz, W., Analog programmable distance calculation circuit for winner takes all neural network realized in the CMOS technology, IEEE Trans. Neural Netw. Learn. Syst., 27, 3, 661-673 (2016) |
| [8] | Talaśka, T.; Kolasa, M.; Długosz, R.; Farine, P. A., An efficient initialization mechanism of neurons for Winner Takes All Neural Network implemented in the CMOS technology, Appl. Math. Comput., 267, 119-138 (2015), Elsevier · Zbl 1410.92013 |
| [9] | Długosz, R.; Talaśka, T.; Pedrycz, W., Current-mode analog adaptive mechanism for ultra-low-power neural networks, IEEE Trans. Circuits Syst. II, 58, 1, 31-35 (2011) |
| [10] | Długosz, R.; Kolasa, M.; Pedrycz, W.; Szulc, M., Parallel programmable asynchronous neighborhood mechanism for kohonen SOM implemented in CMOS technology, IEEE Trans. Neural Netw., 22, 12, 2091-2104 (2011) |
| [11] | Dlugosz, R.; Pedrycz, W., Łukasiewicz fuzzy logic networks and their ultra low power hardware implementation, Neurocomputing, 73 (2010), Elsevier |
| [12] | Talaśka, T.; Dlugosz, R.; Skruch, P., Efficient transistor level implementation of selected fuzzy logic operators used in control systems, (Advances in Intelligent Systems and Computing, Trends in Advanced Intelligent Control, Optimization and Automation, Vol. 577 (2017), Springer) |
| [13] | Talaśka, T., Implementation of fuzzy logic operators as digital asynchronous circuits in CMOS technology, (International Conference on Microelectronics. International Conference on Microelectronics, MIEL (2017), Nis: Nis Serbia) |
| [14] | Jung, W. G., (Walt Jung, Op Amp Applications HandBook (2005), Newnes, Elsevier, Analog Devices), with the technical staff of Analog Devices |
| [15] | (Walt Kester, Analog Devices, Data Conversion HandBook (2005), Newnes, Elsevier, Analog Devices), with the technical staff of Analog Devices, A Volume in the Analog Devices Series |
| [16] | Marche, D.; Savaria. Y. Gagnon, Y., An improved switch compensation technique for inverted R-2R ladder DACs, IEEE Trans. Circuits Syst. I. Regul. Pap., 56, 6, 1115-1124 (2009) |
| [17] | Li, Y.; Chen, D., A novel 20-bit R-2R DAC structure based on ordered element matching, (IEEE International Symposium on Circuits and Systems. IEEE International Symposium on Circuits and Systems, ISCAS (2015)), 1030-1033 |
| [18] | Guo, W.; Abraham, T.; Chiang, S.; Trehan, Ch.; Yoshioka, M.; Sun, N., An area- and power-efficient \(I_{\operatorname{ref}}\) compensation technique for voltage-mode R-2R DACs, IEEE Trans. Circuits Syst. II, 62, 7, 656-660 (2015) |
| [19] | Lin, Y.; Geiger, R., Resistors layout for enhancing yield of R-2R DACs, (IEEE International Symposium on Circuits and Systems (ISCAS), Vol. 5 (2002)), V97-V100 |
| [20] | Tan, Q.; Wei, Q.; Hu, J.; Aldred, D., Road vehicle detection using fuzzy logic rule-based method, (International Conference on Fuzzy Systems and Knowledge Discovery, Vol. 3 (2010)) |
| [21] | Chen, Z.; Gomez, S. A.; McCormick, M., A fuzzy logic controlled power electronic system for variable speed wind energy conversion systems, (International Conference on Power Electronics and Variable Speed Drives (2000)) |
| [22] | Shenguang; He, Adaptive control of dual-motor autonomous steering system for intelligent vehicles via Bi-LSTM and fuzzy methods, Control Eng. Pract., 130 (2023) |
| [23] | Mahmoud, R., A novel metaheuristics with adaptive neuro-fuzzy inference system for decision making on autonomous unmanned aerial vehicle systems, ISA Trans. (2022) |
| [24] | Übeyli, Elif Derya, Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes, Digit. Signal Process., 20 (2010) |
| [25] | Boadh, Rahul, Study and prediction of prostate cancer using fuzzy inference system, Mater. Today: Proc., 56 (2022) |
| [26] | Nilash, Mehrbakhsh, A knowledge-based system for breast cancer classification using fuzzy logic method, Telemat. Inform., 34, 4 (2017) |
| [27] | Yen, J.; Langari, R.; Zadeh, L. A., Industrial Applications of Fuzzy Logic and Intelligent Systems (1995), IEEE Press: IEEE Press New York · Zbl 0864.00015 |
| [28] | Nagaraj, R.; Mayurappriyan, P. S.; Jerome, J., Microcontroller based fuzzy logic technique for dc-dc converter, (International Conference on Power Electronics (2006)) |
| [29] | Ilyas, Afshan, FPGA based real-time implementation of fuzzy logic controller for maximum power point tracking of solar photovoltaic system, Optik, 213 (2020) |
| [30] | Prabakaran, G., FPGA based intelligent embedded system for predicting the productivity using fuzzy logic, Sustain. Comput.: Inf. Syst., 35 (2022) |
| [31] | Guo, S.; Peters, L.; Surmann, H., Design and application of an analog fuzzy logic controller, IEEE Trans. Fuzzy Syst., 4, 4 (1996) |
| [32] | Yamakawa, T.; Miki, T., The current mode fuzzy logic integrated circuits fabricated by the standard CMOS process, IEEE Trans,. Comput., C-35, 2 (1986) |
| [33] | Baturone, I.; Sanchez-Solano, S.; Barriga, A.; Huertas, J., Implementation of CMOS fuzzy controllers as mixed-signal integrated circuits, IEEE Trans. Fuzzy Syst., 5, 1 (1997) |
| [34] | Dlugosz, R.; Iniewski, K., Analog-to-digital converters for radiation detection electronics, (Iniewski, K., Chapter 11 in Electronics for Radiation Detection (Devices, Circuits, and Systems) (2010), CRC Press), 285-312, ISBN-10: 1439816484, ISBN-13: 978-1439816486 |
| [35] | Xing, Shiwei; Wu, Chenjian, Implementation of a neuron using sigmoid activation function with CMOS, (IEEE International Conference on Integrated Circuits and Microsystems. IEEE International Conference on Integrated Circuits and Microsystems, ICICM (2020)) |
| [36] | Moposita, T.; Trojman, L.; Crupi, F.; Lanuzza, M.; Vladimirescu, A., Voltage-to-voltage sigmoid neuron activation function design for artificial neural networks, (IEEE Latin America Symposium on Circuits and System. IEEE Latin America Symposium on Circuits and System, LASCAS (2022)), 1-4 |
| [37] | Vaisnav, A.; Ashok, Sandhya; Vinaykumar, Shatharajupally; Thilagavathy, R., FPGA implementation and comparison of sigmoid and hyperbolic tangent activation functions in an artificial neural network, (International Conference on Electrical, Computer and Energy Technologies. International Conference on Electrical, Computer and Energy Technologies, ICECET (2022)) |
| [38] | Chandra, Mahesh, A novel method for scalable VLSI implementation of hyperbolic tangent function, IEEE Des. Test., 39, 1 (2022) |
| [39] | Nguyen, Thanh Dat; Kim, Dong Hwan; Yang, Jin Seok; Park, Sang Yoon, High-speed ASIC implementation of tanh activation function based on the CORDIC algorithm, (International Technical Conference on Circuits/Systems, Computers and Communications, (ITC-CSCC) (2021)) |
| [40] | Vinyals, Oriol; Friedland, Gerald, A hardware-independent fast logarithm approximation with adjustable accuracy, (IEEE International Symposium on Multimedia (2008)) |
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