SHF: Medium: Quantifying and Designing Around Architectural Risk

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Computer applications and technologies are changing at an ever-increasing rate. This change comes with uncertainty and that uncertainty makes it difficult to make good decisions about how to build systems that are maximally useful in the future. While developing new computer systems has always involved risk, the new magnitude of these uncertainties may now lead to either overly conservative design practices at one end, or designs that have 'fragile' performance at the other end. While risk assessment and management are expected in both business and investment, these aspects are typically treated as independent to questions of performance and efficiency in computer design when in fact they are not. As hardware and software characteristics become uncertain (i.e. samples from a distribution), the resulting performance distributions quickly grow beyond our ability to reason about them with intuition alone. Through the collaboration of computer system designers and experts in the impacts of technology uncertainty, this project is developing new and fundamental techniques for both quantifying risk and optimizing designs in risk-aware ways. This project is working to transform the way in which architectures and systems, from micro to data-center scale, are designed and analyzed. The investigators are creating new technologies, but also making those technologies available and accessible through open repositories, involving undergraduates at all levels in his research, and integrating basic concepts from these statistical design methods into outreach through "I love STEM" and other efforts. This interdisciplinary project is targeted at advancing the frontiers of computer architecture, electronic design automation, and uncertainty quantification. The developed methodologies can also find application far outside the original area of inquiry e.g., impact risk analysis and management across many other engineering systems including renewable energy, robotic systems, and autonomous driving.The work is demonstrating, for the first time, that it is possible to define, model, quantify, and mitigate computer architectural risk. By bridging ideas of risk and risk-management from economics and fast stochastic algorithms from uncertainty quantification, a new framework for high-level risk-aware computer architecture analysis is being created. Efficient techniques, using fewer than 50 data points, can effectively estimate architectural uncertainty to enable the rigorous quantification and management of risk during computer architecture design. This framework is embodied in a symbolic / statistical analysis system that eases the exploration of these surprisingly complex design spaces. A declarative language abstracts away the complexity of new probability-constrained optimization and intelligent sampling methods, while risk-aware micro and macro architectural improvements demonstrate the value of these methods in practice. The methods transform the way one can extract useful models of uncertainty from a limited selections of data points available to each manufacturer, propagate those uncertainties correctly through complex compositions of systems and the interactions of resources, efficiently quantify the impact of those propagated uncertainties on the end figures of merit for the system, encapsulate those methods in a language and solver framework, and develop exemplars of how such uncertainty can be more actively mitigated and controlled.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.