Opening the “Black Box” of Silicon Chip Design in Neuromorphic Computing
Opening the “Black Box” of Silicon Chip Design in Neuromorphic Computing
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DOI：
10.5772/intechopen.83832
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发表时间：
2019-03
期刊：
影响因子：
--
通讯作者：
Kangjun Bai;Y. Yi
中科院分区：
文献类型：
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作者：
Kangjun Bai;Y. Yi
神经形态计算是一种受生物启发的计算架构，它将神经科学转移到硅芯片上，有可能达到与哺乳动物大脑相同的计算水平和能量效率。同时，基于非易失性存储器横杆阵列的三维集成电路设计在神经形态计算设计中独特地揭示了其固有的具有并行计算能力的向量矩阵计算。本章将介绍神经形态计算电子电路设计的最新研究趋势。此外，还将讨论具有延迟反馈拓扑结构的实用仿生尖峰神经网络。为了模仿人类处理信息的方式，我们制造的脉冲神经网络芯片具有直接处理模拟信号的能力，从而在硬件实现成本小的情况下实现高能效。模拟哺乳动物大脑的神经结构，研究了具有记忆突触的3D-IC实现技术的潜力。最后介绍了该方法在混沌时间序列预测和视频帧识别方面的应用。
Neuromorphic computing, a bio-inspired computing architecture that transfers neuroscience to silicon chip, has potential to achieve the same level of computation and energy efficiency as mammalian brains. Meanwhile, threedimensional (3D) integrated circuit (IC) design with non-volatile memory crossbar array uniquely unveils its intrinsic vector-matrix computation with parallel computing capability in neuromorphic computing designs. In this chapter, the state-of-the-art research trend on electronic circuit designs of neuromorphic computing will be introduced. Furthermore, a practical bio-inspired spiking neural network with delay-feedback topology will be discussed. In the endeavor to imitate how human beings process information, our fabricated spiking neural network chip has capability to process analog signal directly, resulting in high energy efficiency with small hardware implementation cost. Mimicking the neurological structure of mammalian brains, the potential of 3D-IC implementation technique with memristive synapses is investigated. Finally, applications on the chaotic time series prediction and the video frame recognition will be demonstrated.