Enabling a New Methodology of Neural Coding: Multiplexing Temporal Encoding in Neuromorphic Computing
Enabling a New Methodology of Neural Coding: Multiplexing Temporal Encoding in Neuromorphic Computing
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DOI：
10.1109/tvlsi.2023.3234514
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发表时间：
2023-03
影响因子：
2.8
通讯作者：
Honghao Zheng;Kangjun Bai;Y. Yi
中科院分区：
文献类型：
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作者：
Honghao Zheng;Kangjun Bai;Y. Yi
从速率到时间编码，尖峰信息处理已经在各种神经形态应用中表现出优势。在数据容量和鲁棒性方面，复用编码优于其他编码方案。在这项工作中，我们的目标是实现一类新的多路复用时间编码器，图案刺激在多个时间尺度，以提高信息处理能力，和鲁棒性的系统部署在嘈杂的环境中。利用阈下膜振荡（SMO）的内部参考帧，编码的尖峰模式对输入噪声不太敏感，提高了编码器的鲁棒性。我们的设计结果在一个巨大的节省功耗和硅面积相比，功耗高的模数转换器。此外，一个工作原型的复用时间编码器建立在一个脉冲间间隔（ISI）编码方案的基础上实现在一个硅芯片上使用标准的180纳米CMOS工艺。据我们所知，我们介绍的编码器演示了第一个集成电路（IC）实现神经编码与多路复用拓扑结构。最后，我们的设计的准确性和效率进行了评估，通过标准的机器学习基准，包括修改后的国家标准与技术研究所（MNIST），加拿大高级研究所（CIFAR）-10，街景门牌号（SVHN），并在高速通信网络中的频谱感知。虽然我们的多路复用时间编码器在所有的基准测试中表现出更高的分类精度，但在有效帧速率为300 MHz的情况下，每个尖峰的功耗和耗散能量分别仅为2.6 μ W $和95 fJ/尖峰。与其他编码方案相比，我们的多路复用时间编码器实现了最多100%的高数据容量，11.4%更准确的分类，和25%更强大的抗噪声。与最先进的设计相比，我们的工作实现了高达105\times $的功率效率，而不显着增加硅面积。
From rate to temporal encoding, spiking information processing has demonstrated advantages across diverse neuromorphic applications. In the aspects of data capacity and robustness, multiplexing encoding outperforms alternative encoding schemes. In this work, we aim to implement a new class of multiplexing temporal encoders, patterning stimuli in multiple timescales to improve the information processing capability, and robustness of systems deployed in noisy environments. Benefitted by the internal reference frame using subthreshold membrane oscillation (SMO), the encoded spike patterns are less sensitive to the input noise, increasing the encoder’s robustness. Our design results in a tremendous saving on power consumption and silicon area compared with the power-hungry analog-to-digital converters. Furthermore, a working prototype of the multiplexing temporal encoder built based on an interspike interval (ISI) encoding scheme is implemented on a silicon chip using the standard 180-nm CMOS process. To the best of our knowledge, our introduced encoder demonstrates the first integrated circuit (IC) implementation of neural encoding with multiplexing topology. Finally, the accuracy and efficiency of our design are evaluated through standard machine learning benchmarks, including Modified National Institute of Standards and Technology (MNIST), Canadian Institute For Advanced Research (CIFAR)-10, Street View House Number (SVHN), and spectrum sensing in high-speed communication networks. While our multiplexing temporal encoder demonstrates a higher classification accuracy across all the benchmarks, the power consumption and dissipated energy per spike reach merely $2.6~\mu \text {W}$ and 95 fJ/spike, respectively, with an effective frame rate of 300 MHz. Compared with alternative encoding schemes, our multiplexing temporal encoder achieves at most 100% higher data capacity, 11.4% more accurate in classification, and 25% more robust against noise. Compared with the state-of-the-art designs, our work achieves up to $105 \times $ power efficiency without significantly increasing the silicon area.