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As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.

 

Heterogeneous chiplets have been proposed for accelerating high-performance computing tasks. Integrated inside one package, CPU and GPU chiplets can share a common interconnection network that can be implemented through the interposer. However, CPU and GPU applications have very different traffic patterns in general. Without effective management of the network resource, some chiplets can suffer significant performance degradation because the network bandwidth is taken away by communication-intensive applications. Therefore, techniques need to be developed to effectively manage the shared network resources. In a chiplet-based system, resource management needs to not only react in real-time but also be cost-efficient. In this work, we propose a reconfigurable network architecture, leveraging Kalman Filter to make accurate predictions on network resources needed by the applications and then adaptively change the resource allocation. Using our design, the network bandwidth can be fairly allocated to avoid starvation or performance degradation. Our evaluation results show that the proposed reconfigurable interconnection network can dynamically react to the changes in traffic demand of the chiplets and improve the system performance with low cost and design complexity. 

Genomic analysis is the study of genes which includes the identification, measurement, or comparison of genomic features. Genomics research is of great importance to our society because it can be used to detect diseases, create vaccines, and develop drugs and treatments. As a type of general-purpose accelerators with massive parallel processing capability, GPUs have been recently used for genomics analysis. Developing GPU-based hardware and software frameworks for genome analysis is becoming a promising research area. To support this type of research, benchmarks are needed that can feature representative, concurrent, and diverse applications running on GPUs. In this work, we created a benchmark suite called Genomics-GPU, which contains 10 widely-used genomic analysis applications. It covers genome comparison, matching, and clustering for DNAs and RNAs. We also adapted these applications to exploit the CUDA Dynamic Parallelism (CDP), a recent advanced feature supporting dynamic GPU programming, to further improve the performance. Our benchmark suite can serve as a basis for algorithm optimization and also facilitate GPU architecture development for genomics analysis.