Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability. However, memristor’s non-reliability issues have been one of the main obstacles for the development of memristor-based artificial neurons and neuromorphic computings. Here, we show a memristor 1R cross-bar array without transistor devices for individual memristor access with low variation, 100% yield, large dynamic range, and fast speed for artificial neuron and neuromorphic computing. Based on the developed memristor, we experimentally demonstrate a memristor-based neuron with leaky-integrate and fire property with excellent reliability. Furthermore, we develop a neuro-memristive computing system based on the short-term memory effect of the developed memristor for efficient processing of sequential data. Our neuro-memristive computing system successfully trains and generates bio-medical sequential data (antimicrobial peptides) while using a small number of training parameters. Our results open up the possibility of memristor-based artificial neurons and neuromorphic computing systems, which are essential for energy-efficient edge computing devices.

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Three-dimensional (3D) error correction coding (ECC) provides volumetrically coupled ECC blocks, the possible errors of which can be controlled powerfully by correcting errors from three different directions, iteratively. However, increased parities cause a large overhead, which limits the application of strong ECC schemes. In this study, a new efficient 3D ECC scheme and its decoding algorithm for holographic data storage were developed and evaluated. The proposed scheme has 3D ECC capability with a relatively low coding overhead, similarly to that of conventional 2D error correcting schemes, such as RSPC. It gives favorable ECC performance with a high efficiency in a page-based recording and retrieving system, which enables the use of a 3D ECC scheme for various applications.

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A modified low-density parity-check decoding scheme for holographic data storage has been developed and evaluated. It compensates the negative effect from neighboring pixels during the iterative decoding process, which improves the overall error-correction performance when pixels are misaligned. A simulation shows that the proposed scheme outperforms a conventional log-likelihood-ratio (LLR) belief-propagation (BP) algorithm.