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Abstract 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. 

With the rapid advance in Deep Neural Networks (DNNs), GPU’s role as a hardware accelerator becomes increasingly important. Due to the GPU’s significant power consumption, developing high- performance and power-efficient GPU systems is a critical challenge. DNN applications need to move a large amount of data between memory and the processing cores, which consumes a great amount of NoC power. However, prior proposed lossless data compressions cannot achieve optimal performance and energy efficiency because they did not take advantage of the error resilience of DNNs. In this work, we propose an NoC architecture that can reduce power consumption without compromising performance and accu- racy. Our technique takes advantage of the error resilience of DNNs as well as the data locality in the floating-point data representation of DNNs. Each data packet is reorganized by grouping data with similar bits such as in the exponents, and redundant bits are sent only once. We further compress the mantissa fields by appropri- ately selecting "proxy" values for data sharing the same exponent. Our evaluation results show that the proposed technique can ef- fectively reduce the amount of data transmitted and lead to better performance and power trade-offs while preserving accuracy. 

In recent years, Network-on-Chip (NoC) has emerged as a promising solution for addressing a critical performance bottleneck encountered in designing large-scale multi-core systems, i.e., data communication. With advancements in chip manufacturing technologies and the increasing complexity of system designs, the task of designing the communication sub- systems has become increasingly challenging. The emergence of hardware accelerators, such as GPUs, FPGAs and ASICs, together with heterogeneous system integration of the CPUs and the accelerators creates new challenges in NoC design. Conventional NoC architectures developed for CPU-based multi- core systems are not able to satisfy the traffic demands of heterogeneous systems. In recent years, numerous research efforts have been dedicated to exploring the various aspects of NoC design in hardware accelerators and heterogeneous systems. However, there is a need for a comprehensive understanding of the current state-of-the-art research in this emerging research area. This paper aims to provide a summary of research work conducted in heterogeneous NoC design. Through this survey, we aim to present a comprehensive overview of the current related research, highlighting key findings, challenges, and future directions in this field. 

As the Next-Generation Sequencing (NGS) techniques need to process enormous amounts of data, cost-efficientfand high-throughput computational analysis is essential in genomicsfstudy. Conventional computing platforms face great challenges to meet these demands due to their limited processing speed and scalability. Hardware accelerators, such as Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), offer transformative solutions to these computational challenges. This paper provides a state-of-the-art review of the roles of hardware accelerators in genomic analysis.We performed a comprehensive and in-depth analysis of cutting-edge genomics hardware accelerators, such as GPUs, FPGAs, and ASICs, in the context of the specific algorithms they aim to enhance. Besides reviewing opportunities in hardware genome acceleration, we also provide insights into the challenges regarding processing speed, cost efficiency, and scalability. 

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.