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

Wu, Chung-Yu; Tsai, Su-Yung

doi:10.1109/cnna.2006.341618

Cited by 2 publications

(1 citation statement)

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“…To implement the associative memory by CNN in analog VLSI [11], two key aspects of Hebb's postulate, locality and cooperativity [12], are fully exploited in ARMCNN [4], [13]. This biology-inspired approach, Hebb's postulate, is different from the previous approaches [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

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

Tsai

Wang

2010

2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA 2010)

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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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Section: Introductionmentioning

confidence: 99%