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Fault-Tolerant Neuromorphic Computing Systems

机译:容错神经形态计算系统

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The emergence of non-volatile memories (NVM) such as resistive-oxide random access memory (RRAM), magnetoresistive random access memory (MRAM), and phase change memory (PCM) enables brain-inspired neuromorphic computing. However, due to immature fabrication process, NVMs are prone to process variations and manufacturing defects, which must be investigated for effective defect-to-fault mapping, high-coverage test generation, and diagnostics-driven yield learning. In this paper, we present a survey of research on fault modeling, test generation methodologies, and fault-tolerant design of neuromorphic computing systems based on RRAM and MRAM.
机译:诸如电阻氧化物随机存取存储器(RRAM),磁阻随机存取存储器(MRAM)和相变存储器(PCM)之类的非易失性存储器(NVM)的出现使大脑灵感的神经形态计算成为可能。但是,由于制造工艺不成熟,NVM容易出现工艺变化和制造缺陷,必须对其进行研究以进行有效的缺陷到缺陷映射,高覆盖率测试生成以及诊断驱动的良率学习。在本文中,我们对基于RRAM和MRAM的神经形态计算系统的故障建模,测试生成方法和容错设计进行了研究调查。

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