CUDA优化的神经网络通过量化关节活动度和人体测量学预测血糖控制
CUDA optimized Neural Network predicts blood glucose control from quantified joint mobility and anthropometrics
摘要：
神经网络训练需要大量的计算，具有明显的瓶颈。计算统一设备架构（CUDA）编程模型允许我们通过将处理工作负载从CPU传递到图形处理单元（GPU）来加速计算。在本文中，我们利用Nvidia GPU的强大功能来并行化训练中涉及的所有计算，使用CUDA和C++加速具有一个隐藏层的反向传播前馈神经网络。这个优化的神经网络的任务是从非侵入性标志物预测糖化血红蛋白（HbA 1c）的水平。糖尿病患病率的增加导致了对早期检测和准确诊断的迫切需要。然而，由于传统测试的侵入性和局限性，正在考虑替代方法。据报道，关节活动受限（LJM）是血糖控制不良的指标。定量手指的LJM，并研究其与HbA 1c的联系以及HbA 1c的其他潜在非侵入性标志物。我们在特立尼达南部的一家诊所收集了120名参与者的33个潜在标记的读数。我们的神经网络在男性参与者的训练集和测试集上分别达到了95.65%和86.67%的准确率，在女性参与者的训练集和测试集上分别达到了97.73%和66.67%的准确率。使用来自Nvidia GeForce GTX 660的960个CUDA核心，我们的并行神经网络在两个子集上的训练速度比在Intel Core（TM）i7- 3630 QM 2.40 GHz CPU上的相应CPU实现快50倍。
英文摘要：
Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing unit (GPU). In this paper, we leveraged the power of Nvidia GPUs to parallelize all of the computation involved in training, to accelerate a backpropagation feed-forward neural network with one hidden layer using CUDA and C++. This optimized neural network was tasked with predicting the level of glycated hemoglobin (HbA1c) from non-invasive markers. The rate of increase in the prevalence of Diabetes Mellitus has resulted in an urgent need for early detection and accurate diagnosis. However, due to the invasiveness and limitations of conventional tests, alternate means are being considered. Limited Joint Mobility (LJM) has been reported as an indicator for poor glycemic control. LJM of the fingers is quantified and its link to HbA1c is investigated along with other potential non-invasive markers of HbA1c. We collected readings of 33 potential markers from 120 participants at a clinic in south Trinidad. Our neural network achieved 95.65% accuracy on the training and 86.67% accuracy on the testing set for male participants and 97.73% and 66.67% accuracy on the training and testing sets for female participants. Using 960 CUDA cores from a Nvidia GeForce GTX 660, our parallelized neural network was trained 50 times faster on both subsets, than its corresponding CPU implementation on an Intel Core (TM) i7-3630QM 2.40 GHz CPU.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
