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New applications such as smart homes, smart cities, and autonomous vehicles are driving an increased interest in deploying machine learning on edge devices. Unfortunately, deploying deep neural networks (DNNs) on resource-constrained devices presents significant challenges. These workloads are computationally intensive and often require cloud-like resources. Prior solutions attempted to address these challenges by either introducing more design efforts or by relying on cloud resources for assistance. In this paper, we propose a runtime adaptive convolutional neural network (CNN) acceleration framework that is optimized for heterogeneous Internet of Things (IoT) environments. The framework leverages spatial partitioning techniques through fusion of the convolution layers and dynamically selects the optimal degree of parallelism according to the availability of computational resources, as well as network conditions. Our evaluation shows that our framework outperforms state-of-art approaches by improving the inference speed and reducing communication costs while running on wirelessly-connected Raspberry-Pi3 devices. Experimental evaluation shows up to 1.9× ∼ 3.7× speedup using 8 devices for three popular CNN models. CCS CONCEPTS • Computing methodologies → Neural networks; Massively parallel algorithms; • Computer systems organization → Embedded hardware.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has recently seen serious increase in the number of affected subjects. In the last decade, neuroimaging has been shown to be a useful tool to understand AD and its prodromal stage, amnestic mild cognitive impairment (MCI). The majority of AD/MCI studies have focused on disease diagnosis, by formulating the problem as classification with a binary outcome of AD/MCI or healthy controls. There have recently emerged studies that associate image scans with continuous clinical scores that are expected to contain richer information than a binary outcome. However, very few studies aim at modeling multiple clinical scores simultaneously, even though it is commonly conceived that multivariate outcomes provide correlated and complementary information about the disease pathology. In this article, we propose a sparse multi-response tensor regression method to model multiple outcomes jointly as well as to model multiple voxels of an image jointly. The proposed method is particularly useful to both infer clinical scores and thus disease diagnosis, and to identify brain subregions that are highly relevant to the disease outcomes. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the proposed method enhances the performance and clearly outperforms the competing solutions.

Mild cognitive impairment (MCI) is a pre-existing state of Alzheimer's disease (AD). An accurate prediction on the conversion from MCI to AD is of vital clinical significance for potential prevention and treatment of AD.Longitudinal studies received widespread attention for investigating the disease progression, though most studies did not sufficiently utilize the evolution information. In this paper, we proposed a cerebral similarity network with more progression information to predict the conversion from MCI to AD efficiently. First, we defined the new dynamic morphological feature to mine longitudinal information sufficiently. Second, based on the multiple dynamic morphological features the cerebral similarity network was constructed by sparse regression algorithm with optimized parameters to obtain better prediction performance. Then, leave-one-out cross-validation and support vector machine (SVM) were employed for the training and evaluation of the classifiers. The proposed methodology obtained a high accuracy of 92.31% (Sensitivity=100%, Specificity=82.86%) in a three-year ahead prediction of MCI-to-AD conversion. Experiment results suggest the effectiveness of the dynamic morphological feature, serving as a more sensitive biomarker in the prediction of MCI conversion.

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