Designing Right-Provisioned System Architectures for Edge Computing

The growth of the Internet of Things (IoT) technology is transforming various industry sectors. Millions of smart devices, sensors, and actuators collaborate to monitor and manage the physical environment and human systems in the IoT paradigm. The traditional IoT is designed as a distributed system, with low-power edge devices collecting data and transmitting it to the centralized high-performance head nodes. The head nodes analyze the data, help with data visualization, and generate actionable information. However, significant challenges and overheads like bandwidth bottleneck and latency increase arise from the continuous data transmission between the edge and head devices. Edge computing is an emerging solution to the problems associated with traditional IoT. In edge computing, the computation is moved closer to the edge devices by equipping them with sufficient computation capabilities. Edge computing spans a wide variety of applications, application domains, and devices. In this dissertation, we focus on developing efficient system architectures for the edge devices. The first step in optimizing the system architectures is to understand the requirements of the target applications. We need to characterize the applications on a system to understand the computational requirements and derive insights about the system provisioning and identify potential optimization opportunities. It is essential to characterize a variety of applications and benchmark suites since each application has different computational demands, can stress different system components, and help us better understand the system requirements. In this dissertation, we choose one of the high-impact IoT application domain suitable for edge computing, the Internet of Medical Things (IoMT), as a use-case. We propose a benchmark suite consisting of representative IoMT applications and analyze them for different execution characteristics to derive insights into their compute and memory requirements. We also present workload characterization studies of two well-known and diverse benchmark suites: SPEC CPU2017, which aims to assess the systems' high-performance computing capabilities, and GAP, which comprises of memory-bound graph applications that are critical components of data analytics workflows. Edge computing devices will typically have strict area and power budgets, and hence, employ low-energy microprocessors with fewer computing resources. Moreover, the current generation of microprocessors has hit performance and power walls due to technology scaling slowdown. Current microprocessors' inability to sustain the historically observed performance scaling has resulted in finding alternatives to improve the target application's execution efficiency. Domain-specific architectures that efficiently utilize hardware acceleration to improve the target application's execution are a good fit for edge computing. As such, we present a domain-specific architecture for an electrocardiogram-based biometric authentication application that improves the performance and energy, compared to the baseline processor, and mitigates timing-based side-channel attack vulnerabilities. The major obstacle in designing domain-specific architectures is that we need to modify application codes to access the accelerators. To address this issue, we propose a programmer-agnostic LLVM-based methodology for generating domain-specific accelerators. Our methodology identifies and ranks the recurrent and similar code blocks within a set of applications that would benefit the most from hardware acceleration, and then integrates the corresponding accelerators into the system to generate domain-specific architectures. Using the methodology, we present a performance and energy-efficient domain-specific architecture for the IoMT applications

Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators

The growing numbers of application areas for artificial intelligence (AI) methods have led to an explosion in availability of domain-specific accelerators, which struggle to support every new machine learning (ML) algorithm advancement, clearly highlighting the need for a tool to quickly and automatically transition from algorithm definition to hardware implementation and explore the design space along a variety of SWaP (size, weight and Power) metrics. The software defined architectures (SODA) synthesizer implements a modular compiler-based infrastructure for the end-to-end generation of machine learning accelerators, from high-level frameworks to hardware description language. Neuromorphic computing, mimicking how the brain operates, promises to perform artificial intelligence tasks at efficiencies orders-of-magnitude higher than the current conventional tensor-processing based accelerators, as demonstrated by a variety of specialized designs leveraging Spiking Neural Networks (SNNs). Nevertheless, the mapping of an artificial neural network (ANN) to solutions supporting SNNs is still a non-trivial and very device-specific task, and completely lacks the possibility to design hybrid systems that integrate conventional and spiking neural models. In this paper, we discuss the design of such an integrated generator, leveraging the SODA Synthesizer framework and its modular structure. In particular, we present a new MLIR dialect in the SODA frontend that allows expressing spiking neural network concepts (e.g., spiking sequences, transformation, and manipulation) and we discuss how to enable the mapping of spiking neurons to the related specialized hardware (which could be generated through middle-end and backend layers of the SODA Synthesizer). We then discuss the opportunities for further integration offered by the hardware compilation infrastructure, providing a path towards the generation of complex hybrid artificial intelligence systems

Potentiation of carbon tetrachloride hepatotoxicity and lethality in type 2 diabetic rats

ABSTRACT There is a need for well characterized and economical type 2 diabetic model that mimics the human disease. We have developed a type 2 diabetes rat model that closely resembles the diabetic patients and takes only 24 days to develop robust diabetes. Nonlethal doses of allyl alcohol (35 mg/kg i.p.), CCl 4 (2 ml/kg i.p.), or thioacetamide (300 mg/kg i.p.) yielded 80 to 100% mortality in diabetic rats. The objective of the present study was to investigate two hypotheses: higher CCl 4 bioactivation and/or inhibited compensatory tissue repair were the underlying mechanisms for increased CCl 4 hepatotoxicity in diabetic rats. Diabetes was induced by feeding high fat diet followed by a single dose of streptozotocin on day 14 (45 mg/kg i.p.) and was confirmed on day 24 by hyperglycemia, normoinsulinemia, and oral glucose intolerance. Time course studies (0 -96 h) of CCl 4 (2 ml/kg i.p.) indicated that although initial liver injury was the same in nondiabetic and diabetic rats, it progressed only in the latter, culminating in hepatic failure, and death. Hepatomicrosomal CYP2E1 protein and activity, lipid peroxidation, glutathione, and 14 CCl 4 covalent binding to liver tissue were the same in both groups, suggesting that higher bioactivation-based injury is not the mechanism. Inhibited tissue repair resulted in progression of injury and death in diabetic rats, whereas in the nondiabetic rats robust tissue repair resulted in regression of injury and survival after CCl 4 administration. These studies show high sensitivity of type 2 diabetes to model hepatotoxicants and suggest that CCl 4 hepatotoxicity is potentiated due to inhibited tissue repair. Several animal models resembling type 2 diabetes either occur spontaneously or can be induced experimentally. Most of the commonly used models of type 2 diabetes are genetic and have the disadvantage of prohibitive costs, unavailability, and failure to represent etiology of human disease. Consumption of high fat diet leads to insulin resistance and is considered to be a major predisposing factor for type 2 diabetes To address this need, we have refined and characterized an existing model based on high fat diet and a single dose of streptozotocin (STZ, 45 mg/kg i.p.). The principle behind the development of type 2 diabetes is simple. High fat diet elicits insulin resistance, and the rats maintain normoglycemia due to compensatory hyperinsulinemia. Administration of STZ (45 mg/kg i.p.) decreases insulin levels, destroying a population of pancreatic ␤-cells such that the insulin-resistant rats are now unable to maintain normal glucose levels and develop hyperglycemia, even though insulin levels in these rats are comparable with normal diet-fed normoglycemic rats. This is exactly what is seen in human diabetes where insulin resistance precedes hyperglycemia, thereby making this model a good representative of human type 2 diabetic (DB) condition. Article, publication date, and citation information can be found at http://jpet.aspetjournals.org. DOI: 10.1124/jpet.103.058834. ABBREVIATIONS: STZ, streptozotocin; DB, diabetic; NDB, nondiabetic; ND ϩ STZ, normal diet-fed rats injected streptozotocin; TA, thioacetamide; AA, allyl alcohol; SD, Sprague-Dawley; 3 H-T, tritiated thymidine; HFD, high fat diet-fed rats injected citrate buffer; HFD ϩ STZ, high fat diet-fed rats injected streptozotocin; ND, normal diet fed rats injected citrate buffer; ALT, alanine aminotransferase; AST, aspartate aminotransferase; PCNA, proliferating cell nuclear antigen; MES, 2-(N-morpholino)ethanesulfonic acid