The design of ratio-memory cellular neural network (RMCNN) with self-feedback template weight for pattern learning and recognition

This paper proposes the diffusion circuit for Autonomous Ratio-Memory Cellular Nonlinear Networks (ARMCNNs). ARMCNNs can tolerate large variations of ratio weights which has been shown in our previous paper. However, in our previous circuit implementation, the synapse weight circuit between neighboring neurons was composed of two voltage to current converters (V/Is) and current mirrors. The layout area is still too large for a high density CNN array. Another issue is that for each subsystem of ARMCNNs, spurious memory points may exist besides two binary equilibrium points. The occurence of these spurious memory points will reduce the recognition rate (RR). So this paper proposes the diffusion circuit for synapse weights to extend the domain of attraction (DOA) and therefore eliminate these spurious memory points in comparison with our previous paper.In the literature, MOSFET transistors for the synapse weight circuit mostly either work in the weak inversion region, or in the strong inversion, but not both. Hence, the gate voltage has to be carefully desgined for MOSFET transistors working in the correct regions. On the contrary, in this paper, the synapse weight of a single MOSFET can work in either the weak inversion region or the strong inversion, making analog design more robust.

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A learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for associative memory applications

Lai

Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, 2004. ICECS 2004.

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A self-feedback ratio-memory cellular nonlinear network (SRMCNN) with the B template and the modified Hebbian learning algorithm to learn and recognize the image patterns is proposed and analyzed. In the proposed SRMCNN, the coefficients of space-variant B templates are determined from the exemplar patterns during the learning period. The weights are the ratio of the absolute summation of its neighborhood weights in the B templates was stored in the associative memory. This SRMCNN can recognize the learned patterns with distinct white-black noise and output the correct patterns. The Matlab and HSPICE software has been simulated the operation of the proposed SRMCNN. It is shown that the 18×18 SRMCNN can successfully learned and recognized 8 incompletely noisy patterns. As compared to other learnable CNN as associate memories, the proposed SRMCNN could improve pattern learning and recognition capability. The architecture can be implemented in nano-CMOS technology for giga-scale learning system in the real-time applications.

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The design of ratio-memory cellular neural network (RMCNN) with self-feedback template weight for pattern learning and recognition

Cited by 4 publications

References 11 publications

Autonomous Ratio-Memory Cellular Nonlinear Network (ARMCNN) for Pattern Learning and Recognition

Autonomous Ratio-Memory Cellular Nonlinear Network (ARMCNN) for Pattern Learning and Recognition

On the diffusion model for Autonomous Ratio-Memory Cellular Nonlinear Network for pattern recognition

A learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for associative memory applications

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