超导感知机类神经网络的数字-模拟概念
Digital-analog concept for superconducting perceptron-like neural networks
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中文总结 AI 辅助
本文提出一种混合数字-模拟超导感知机架构，通过DAD转换器与$\Sigma$-神经元实现非线性处理，实验测得S形特性，系统仿真验证可行性，并在MNIST上达到97.0%和91.9%的准确率。
中文摘要 AI 辅助
实现超导人工神经网络的一条有前景的途径是采用混合数字-模拟架构，该架构将数字单通量量子（SFQ）通信与紧凑的模拟非线性处理相结合。本研究聚焦于离散信号通过数字-模拟-数字（DAD）转换器时的动态转换过程，其中模拟单元由具有非线性传递函数的$\Sigma$-神经元承担——这是感知机类神经网络的基本单元。此外，作为混合架构的基本功能模块，DAD转换器结合了数模转换器（DAC）和模数转换器（ADC），并将模拟$\Sigma$-神经元的波形重新编码为SFQ脉冲序列。电路级仿真展示了由SFQ脉冲序列编码的输入值如何转换为模拟信号电平，经$\Sigma$-神经元变换后，再映射回基于脉冲的输出。作为关键的实验步骤，我们制造并表征了一个重新设计的$\Sigma$-神经元，并测量了适合实现激活函数的类S形传递特性。将提取的响应纳入系统级仿真，以评估实际器件参数对转换过程的影响。我们描述了DAC、神经元和ADC模块的工作范围匹配要求，支持所提出的接口作为具有数字输入和输出的感知机类超导神经网络构建模块的可行性。最后，我们开发了两个感知机网络，一个使用数学S形激活函数，另一个使用实测的$\Sigma$-神经元传递特性，在MNIST手写数字数据集上分别达到了$97.0\\%$和$91.9\\%$的分类准确率。
英文摘要
A promising route to superconducting artificial neural networks is a hybrid digital-Analog architecture that combines digital single-flux-quantum (SFQ) communication with compact Analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-Analog-to-digital (DAD) converter, in which the role of the Analog cell was performed by a $Σ$-neuron with a nonlinear transfer function -- the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an Analog-to-digital converter (ADC), and re-encodes the Analog $Σ$-neuron waveforms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $Σ$-neuron, and mapped back to pulse-based outputs. As a key experimental step, we fabricated and characterised a redesigned $Σ$-neuron and measured a sigmoid-like transfer characteristic suitable for activation-function implementation. The extracted response was incorporated into system-level simulations to assess the influence of realistic device parameters on the conversion process. We delineate the operating-range matching requirements for the DAC, neuron, and ADC blocks, supporting the feasibility of the proposed interface as a building block for perceptron-like superconducting neural networks with digital inputs and outputs. Finally, we developed two perceptron networks, one using a mathematical sigmoid activation and the other the measured $Σ$-neuron transfer characteristic, which reached classification accuracies of $97.0\%$ and $91.9\%$, respectively, on the MNIST handwritten digit dataset.
发表机构
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University（莫斯科国立大学索博列夫核物理研究所）
- Moscow Technical University of Communications and Informatics (MTUCI)（莫斯科通信与信息技术大学）
- Superconducting Quantum Computing Lab, Quantum Center, Skolkovo（斯科尔科沃量子中心超导量子计算实验室）
- Kotelnikov Institute of Radioengineering and Electronics（科尔涅利尼科夫无线电工程与电子学研究所）
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