This brief proposes a new automatic model parameter selection approach for determining the optimal configuration of high-speed analog-to-digital converters (ADCs) using a combination of particle swarm optimization (PSO) and stochastic gradient descent (SGD) algorithm. The proposed hybrid method first initializes the PSO algorithm to search for optimal neural-network configuration via the particles moving in finite search space with coarse quantization. Using the PSO estimates, the SGD algorithm then finds the global optimum solution. The global search ability of the PSO algorithm and the local search ability of the SGD are thus exploited to determine an optimal solution that is close to the global optimum with reduced latency. Several experiments were constructed to optimize the non-linearities in Nyquist flash and pipeline ADC datasets to show that the neural networks trained by the PSO-SGD algorithm outperform the random search-based performance optimization. Comparative resource analysis of the proposed algorithm is also conducted against the state-of-the-art that highlights improved latencies and performance with similar area and implementation complexity.

中文翻译：

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基于神经网络的高速ADC多参数优化自初始化算法

本简介提出了一种新的自动模型参数选择方法，用于结合使用粒子群优化 (PSO) 和随机梯度下降 (SGD) 算法来确定高速模数转换器 (ADC) 的最佳配置。所提出的混合方法首先初始化 PSO 算法以通过粒子在有限搜索空间中移动并进行粗量化来搜索最佳神经网络配置。然后使用 PSO 估计，SGD 算法找到全局最优解。因此，利用 PSO 算法的全局搜索能力和 SGD 的局部搜索能力来确定接近全局最优且延迟减少的最优解。构建了多个实验来优化 Nyquist 闪存和流水线 ADC 数据集中的非线性，以表明由 PSO-SGD 算法训练的神经网络优于基于随机搜索的性能优化。所提出的算法的比较资源分析也针对最先进的技术进行，该技术突出了具有相似面积和实现复杂性的改进的延迟和性能。