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.

AI start-ups Crossbar, Gyrfalcon Technology, Neural Networks Corporation and Robosensing are getting together to deliver an AI platform and standard for edge computing, gateways, cloud and data centers. The group, called SCAiLE (SCalable AI for Learning at the Edge), is already working with Japanese authorities to review opportunities for the 2020 Olympics, including video-based event detection and response capability. The organization will combine advanced acceleration hardware, resistive memory (ReRAM), optimized neural networks to create ready-made, power-efficient solutions with unsupervised learning and event recognition capability. The consortium addresses the restrictions of traditional AI methodologies that depend on classification of data. The huge growth of IoT systems including thousands of remote edge devices such as sensor-equipped cameras creates a torrent of unstructured information in multiple forms that pours into cloud-located servers and that cannot be handled effectively by classification alone.