Neuromorphic Computing Systems: From CMOS To Emerging Nonvolatile Memory
Neuromorphic Computing Systems: From CMOS To Emerging Nonvolatile Memory
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DOI：
10.2197/ipsjtsldm.12.53
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Chaofei Yang;Ximing Qiao;Yiran Chen
中科院分区：
文献类型：
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作者：
Chaofei Yang;Ximing Qiao;Yiran Chen
摩尔定律的终结和冯·诺依曼瓶颈促使研究人员寻求替代架构，以满足传统计算范式无法轻松实现的对计算资源日益增长的需求。作为一种重要的实践，神经形态计算系统（NCS）被提出来模仿神经元和突触的生物行为，并加速神经网络的计算。然而，传统的基于CMOS的NCS实现受到精确复制生物特性所需的大量硬件成本的影响。近十年来，新兴的非易失性存储器（eNVM）以其高计算效率和高集成度被引入到网络控制系统的设计中。与构建在其他纳米级器件上的电路类似，基于eNVM的NCS也存在许多可靠性问题。在本文中，我们给出了一个简短的调查基于CMOS和eNVM的网络控制系统，包括其基本实现和各种应用中的训练和推理方案。我们还讨论了这些网络控制系统的设计挑战，并介绍了一些技术，可以提高网络控制系统的可靠性，精度，可扩展性和安全性。最后，我们提供了我们的设计趋势和未来的挑战NCS的见解。
: The end of Moore’s Law and von Neumann bottleneck motivate researchers to seek alternative architectures that can fulfill the increasing demand for computation resources which cannot be easily achieved by traditional computing paradigm. As one important practice, neuromorphic computing systems (NCS) are proposed to mimic biological behaviors of neurons and synapses, and accelerate computation of neural networks. Traditional CMOS-based implementation of NCS, however, are subject to large hardware cost required to precisely replicate the biological properties. In very recent decade, emerging nonvolatile memory (eNVM) was introduced to NCS design due to its high computing e ffi ciency and integration density. Similar to the circuits built on other nanoscale devices, eNVM-based NCS also su ff ers from many reliability issues. In this paper, we give a short survey about CMOS-and eNVM-based NCS, including their basic implementations and training and inference schemes in various applications. We also discuss the design challenges of these NCS and introduce some techniques that can improve the reliability, precision, scalability, and security of the NCS. At the end, we provide our insights on the design trend and future challenges of the NCS.