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1. Towards Full Stack Adaptivity in Permissioned Blockchains

   https://doi.org/10.14778/3641204.3641216  

   Wu, Chenyuan; Amiri, Mohammad Javad; Qin, Haoyun; Mehta, Bhavana; Marcus, Ryan; Loo, Boon Thau (January 2024, Proceedings of the VLDB Endowment)

   This paper articulates our vision for a learning-based untrustworthy distributed database. We focus on permissioned blockchain systems as an emerging instance of untrustworthy distributed databases and argue that as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof permissioned blockchain systems need to be designed withfull-stack adaptivityin mind. At the application level, a future-proof system must adaptively learn the best-performing transaction processing paradigm and quickly adapt to new hardware and unanticipated workload changes on the fly. Likewise, the Byzantine consensus layer must dynamically adjust itself to the workloads, faulty conditions, and network configuration while maintaining compatibility with the transaction processing paradigm. At the infrastructure level, cloud providers must enable cross-layer adaptation, which identifies performance bottlenecks and possible attacks, and determines at runtime the degree of resource disaggregation that best meets application requirements. Within this vision of the future, our paper outlines several research challenges together with some preliminary approaches. 

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2. Leapfrogging Medical AI in Low-Resource Contexts Using Edge Tensor Processing Unit

   https://doi.org/10.1109/HI-POCT54491.2022.9744071  

   Sinha, Priyanshu; Gichoya, Judy W.; Purkayastha, Saptarshi (March 2022, 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT))

   With each passing year, the state-of-the-art deep learning neural networks grow larger in size, requiring larger computing and power resources. The high compute resources required by these large networks are alienating the majority of the world population that lives in low-resource settings and lacks the infrastructure to benefit from these advancements in medical AI. Current state-of-the-art medical AI, even with cloud resources, is a bit difficult to deploy in remote areas where we don’t have good internet connectivity. We demonstrate a cost-effective approach to deploying medical AI that could be used in limited resource settings using Edge Tensor Processing Unit (TPU). We trained and optimized a classification model on the Chest X-ray 14 dataset and a segmentation model on the Nerve ultrasound dataset using INT8 Quantization Aware Training. Thereafter, we compiled the optimized models for Edge TPU execution. We find that the inference performance on edge TPUs is 10x faster compared to other embedded devices. The optimized model is 3x and 12x smaller for the classification and segmentation respectively, compared to the full precision model. In summary, we show the potential of Edge TPUs for two medical AI tasks with faster inference times, which could potentially be used in low-resource settings for medical AI-based diagnostics. We finally discuss some potential challenges and limitations of our approach for real-world deployments. 

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3. High-Level Synthesis Based FPGA Hardware Architecture for PCA+SVM for Real-Time Processing on Edge Computing Platforms

   https://doi.org/10.1109/ACCESS.2025.3645763  

   Mohsin, Mokhles A; Perera, Darshika G (January 2025, IEEE Access)

   In the IoT and smart systems era, the massive amount of data generated from various IoT and smart devices are often sent directly to the cloud infrastructure for processing, analyzing, and storing. While handling this big data, conventional cloud infrastructure encounters many challenges, e.g., scarce bandwidth, high latency, real-time constraints, high power, and privacy issues. The edge-centric computing is transpiring as a synergistic solution to address these issues of cloud computing, by enabling processing/analyzing the data closer to the source of the data or at the network’s edge. This in turn allows real-time and in-situ data analytics and processing, which is imperative for many real-world IoT and smart systems, such as smart cars. Since the edge computing is still in its infancy, innovative solutions, models, and techniques are needed to support real-time and in-situ data processing and analysis of edge computing platforms. In this research work, we introduce a novel, unique, and efficient FPGA-HLS-based hardware accelerator for PCA+SVM model for real-time processing and analysis on edge computing platforms. This is inspired by our previous work on PCA+SVM models for edge computing applications. It was demonstrated that the amalgamation of principal component analysis (PCA) and support vector machines (SVM) leads to high classification accuracy in many fields. Also, machine learning techniques, such as SVM, can be utilized for many edge tasks, e.g. anomaly detection, health monitoring, etc.; and dimensionality reduction techniques, such as PCA, are often used to reduce the data size, which in turn vital for memory-constrained edge devices/platforms. Furthermore, our previous works demonstrated that FPGA’s many traits, including parallel processing abilities, low latency, and stable throughput despite the workload, make FPGAs suitable for real-time processing of edge computing applications/platforms. Our proposed FPGA-HLS-based PCA+SVM hardware IP achieves up to 254x speedup compared to its embedded software counterpart, while maintaining small area and low power requirements of edge computing applications. Our experimental results show great potential in utilizing FPGA-based architectures to support real-time processing on edge computing applications. 

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4. Varieties of Data-Centric Science: Regional Climate Modeling and Model Organism Research

   https://doi.org/10.1017/psa.2021.50  

   Lloyd, Elisabeth; Lusk, Greg; Gluck, Stuart; McGinnis, Seth (October 2022, Philosophy of Science)

   Abstract Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains. 

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5. A Survey of Resource Management for Processing-In-Memory and Near-Memory Processing Architectures

   https://doi.org/10.3390/jlpea10040030  

   Khan, Kamil; Pasricha, Sudeep; Kim, Ryan Gary (December 2020, Journal of Low Power Electronics and Applications)

   null (Ed.)

   Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory processing (NMP) paradigms, aims to accelerate these types of applications by moving the computation closer to the data. Over the past few years, researchers have proposed various memory architectures that enable DCC systems, such as logic layers in 3D-stacked memories or charge-sharing-based bitwise operations in dynamic random-access memory (DRAM). However, application-specific memory access patterns, power and thermal concerns, memory technology limitations, and inconsistent performance gains complicate the offloading of computation in DCC systems. Therefore, designing intelligent resource management techniques for computation offloading is vital for leveraging the potential offered by this new paradigm. In this article, we survey the major trends in managing PIM and NMP-based DCC systems and provide a review of the landscape of resource management techniques employed by system designers for such systems. Additionally, we discuss the future challenges and opportunities in DCC management. 

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