Behavioral an real-time verification of a pipeline in the COSMA environment

The case study analyzed in the paper illustrates the example of model checking in the COSMA environment. The system itself is a three-stage pipeline consisting of mutually concurrent modules which also compete for a shared resource. System components are specified in terms of Concurrent State Machines (CSM) The paper shows verification of behavioral properties, model reduction technique, analysis of counter-example and checking of real time properties

Formal Verification of a MESI-based Cache Implementation

Cache coherency is crucial to multi-core systems with a shared memory programming model. Coherency protocols have been formally verified at the architectural level with relative ease. However, several subtle issues creep into the hardware realization of cache in a multi-processor environment. The assumption, made in the abstract model, that state transitions are atomic, is invalid for the HDL implementation. Each transition is composed of many concurrent multi-core operations. As a result, even with a blocking bus, several transient states come into existence. Most modern processors optimize communication with a split-transaction bus, this results in further transient states and race conditions. Therefore, the design and verification of cache coherency is increasingly complex and challenging. Simulation techniques are insufficient to ensure memory consistency and the absence of deadlock, livelock, and starvation. At best, it is tediously complex and time consuming to reach confidence in functionality with simulation. Formal methods are ideally suited to identify the numerous race conditions and subtle failures. In this study, we perform formal property verification on the RTL of a multi-core level-1 cache design based on snooping MESI protocol. We demonstrate full-proof verification of the coherence module in JasperGold using complexity reduction techniques through parameterization. We verify that the assumptions needed to constrain inputs of the stand-alone cache coherence module are satisfied as valid assertions in the instantiation environment. We compare results obtained from formal property verification against a state-of-the-art UVM environment. We highlight the benefits of a synergistic collaboration between simulation and formal techniques. We present formal analysis as a generic toolkit with numerous usage models in the digital design process

Reining in the Functional Verification of Complex Processor Designs with Automation, Prioritization, and Approximation

Our quest for faster and efficient computing devices has led us to processor designs with enormous complexity. As a result, functional verification, which is the process of ascertaining the correctness of a processor design, takes up a lion's share of the time and cost spent on making processors. Unfortunately, functional verification is only a best-effort process that cannot completely guarantee the correctness of a design, often resulting in defective products that may have devastating consequences.Functional verification, as practiced today, is unable to cope with the complexity of current and future processor designs. In this dissertation, we identify extensive automation as the essential step towards scalable functional verification of complex processor designs. Moreover, recognizing that a complete guarantee of design correctness is impossible, we argue for systematic prioritization and prudent approximation to realize fast and far-reaching functional verification solutions. We partition the functional verification effort into three major activities: planning and test generation, test execution and bug detection, and bug diagnosis. Employing a perspective we refer to as the automation, prioritization, and approximation (APA) approach, we develop solutions that tackle challenges across these three major activities. In pursuit of efficient planning and test generation for modern systems-on-chips, we develop an automated process for identifying high-priority design aspects for verification. In addition, we enable the creation of compact test programs, which, in our experiments, were up to 11 times smaller than what would otherwise be available at the beginning of the verification effort. To tackle challenges in test execution and bug detection, we develop a group of solutions that enable the deployment of automatic and robust mechanisms for catching design flaws during high-speed functional verification. By trading accuracy for speed, these solutions allow us to unleash functional verification platforms that are over three orders of magnitude faster than traditional platforms, unearthing design flaws that are otherwise impossible to reach. Finally, we address challenges in bug diagnosis through a solution that fully automates the process of pinpointing flawed design components after detecting an error. Our solution, which identifies flawed design units with over 70% accuracy, eliminates weeks of diagnosis effort for every detected error.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137057/1/birukw_1.pd

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Major league baseball fans’ climate change attitudes and willingness to adapt: climate vulnerability vs. America’s pastime.

Climate change threatens the ability to enjoy sport around the world, including in the United States. While the scientific community reached consensus regarding the presence and severity of climate change near the turn of the twenty-first century, that same agreement has not been met across the American general public. Major League Baseball (MLB) is particularly vulnerable to climate change in the U.S. due to its season duration, geographic footprint, and largely outdoor nature. Therefore, the purpose of this study was to investigate relationships between U.S.-based MLB fans’ sport identification and their climate change attitudes, perceptions of climate change risk, and willingness to adapt. Specifically, this study sought to advance climate change perception research by focusing on sport fans in a sport context, groups that are understudied in climate change and sport ecology research. Using social identity theory to frame the significance of sport identification, this study aimed to model transitions from cognition to action for MLB fans. Social identity theory served to explain how an individual creates meaning about the world around them, in this instance climate change, by the social groups to which they voluntarily belong, that is sport identification. A cross-sectional survey design was used to address the study’s purpose. The questionnaire was designed and hosted on Qualtrics Survey Software, but distributed as a Human Intelligence Task on Amazon’s Mechanical Turk. The questionnaire contained items to measure fans’ attitudes, general risk perceptions, sport-specific risk perceptions, and willingness to adapt. Participant responses (n = 540) indicated personal experiences with extreme weather most strongly influenced general climate change risk perceptions. Further, responses revealed fans who had general climate change risk perceptions were more likely to have sport-specific risk perceptions. This relationship was not moderated by sport identification, but sport identification did significantly predict sport-specific risk perceptions. Likewise, sport identification did not moderate the relationship between fans’ sport-specific climate change risk perceptions and their willingness to adapt. However, responses revealed fans who perceived climate change risks to the sport were more willing to adapt their behaviors to climate change. As a result of these findings, there were several theoretical and practical implications. Theoretically, although sport identification did not moderate the hypothesized relationships, social identity theory does serve as an avenue to explore the connections between sport fans and the realities of climate change on sport. The overall model structure was supported, indicating the possibility to examine found relationships through additional theoretical lenses. The findings revealed a direct connection between sport consumer behavior research and climate change, opening new avenues for researchers within sport management and climate research. From a practical standpoint, this study found early empirical evidence to support the United Nations’ suggestion that sport fans are critical to engaging in, and accelerating, climate action in the sport sector. Additionally, this study’s findings suggest pro-environmental efforts pertaining to climate adaptation in MLB should include fans, and the UN should invest in educational awareness regarding climate change risks to sport for fans