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Recent progress in artificial synaptic devices: materials, processing and applications

机译:最近人工突触装置的进展:材料,加工和应用

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Artificial synapses are memristor-based devices mimicking biological synapses, and they are used in neuromorphic computing systems that process information in a parallel, energy efficient way and store information in an analog, non-volatile form. The next generation of computing systems are anticipated to use memristive circuits, as they can overcome the shortcomings of the von Neumann computer architecture in which the levels of memory and the CPU are separated, creating a bottleneck that causes energy-loss during information transfer. Memristors are utilized to build Resistive Random Access Memory (RRAM) that allows for multi-level data storage and construction of self-correcting, autonomous learning systems that can solve complex computational tasks that have historically required super-computing hardware. Artificial synapses have received attention since HP Labs fabricated the first practical memristor device. In this review we summarize the working principles, device architectures, fabrication and processing techniques, as well as the strategies for materials selection including binary metal oxide, perovskite, polymer, and organic materials. We also discuss the applications and challenges of using artificial synapses in artificial intelligence tasks such as image recognition, tactile sensing and speech recognition.
机译:人工突触是模仿生物突触的忆阻器设备,用于神经形态计算系统,以并行、节能的方式处理信息,并以模拟、非易失性的形式存储信息。预计下一代计算系统将使用忆阻电路,因为它们可以克服冯·诺依曼计算机体系结构的缺点,在这种体系结构中,内存和CPU的级别是分开的,这会造成瓶颈,导致信息传输过程中的能量损失。忆阻器用于构建电阻式随机存取存储器(RRAM),该存储器允许多级数据存储和构建自校正、自主学习系统,该系统可以解决复杂的计算任务,这些任务在历史上需要超级计算硬件。自从惠普实验室制造出第一个实用的忆阻器设备以来,人工突触就受到了关注。本文综述了二元金属氧化物、钙钛矿、聚合物和有机材料的工作原理、器件结构、制备和加工技术,以及材料选择的策略。我们还讨论了人工突触在图像识别、触觉感知和语音识别等人工智能任务中的应用和挑战。

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