We present the design and development of the automata processor, a massively parallel non-von Neumann semiconductor architecture that is purpose-built for automata processing. This architecture can directly implement non-deterministic finite automata in hardware and can be used to implement complex regular expressions, as well as other types of automata which cannot be expressed as regular expressions. We demonstrate that this architecture exceeds the capabilities of high-performance FPGA-based implementations of regular expression processors. We report on the development of an XML-based language for describing automata for easy compilation targeted to the hardware. The automata processor can be effectively utilized in a diverse array of applications driven by pattern matching, such as cyber security and computational biology.
Executive SummaryAs wind power facilities age, project owners are faced with plant end-of-life decisions. This report is intended to inform policymakers and the business community regarding the history, opportunities, and challenges associated with plant end of life actions, in particular, repowering. Specifically, the report details the history of repowering, examines the plant age at which repowering becomes financially attractive, and estimates the incremental market investment and supply chain demand that might result from future U.S. repowering activities.Repowering as defined here includes two types of actions. Full repowering refers to the complete dismantling and replacement of turbine equipment at an existing project site. Partial repowering is defined as installing a new drivetrain and rotor on an existing tower and foundation. Partial repowering allows existing wind power projects to be updated with equipment that increases energy production, reduces machine loads, increases grid service capabilities, and improves project reliability at lower cost and with reduced permitting barriers relative to full repowering and greenfield projects.Repowering first emerged in the early 1990s in the California and Danish wind power markets and was followed by the Dutch and German markets in the 1990s and 2000s. Although repowering activity has occurred elsewhere, these locales remain the principal markets for repowering investments. Historically, repowering has been viewed as a means of increasing project productivity while offering an array of other potential attributes of interest. Fundamentally, however, profitability for a given project is the primary driver of repowering decisions. Given limited financing, the anticipated profitability at alternate greenfield sites is also relevant.Two distinct analyses were conducted to understand the plant age when repowering becomes viable. These analyses utilized NREL's System Advisor Model (SAM), a tool that enables the user to predict estimated cash flows from a variety of electric power generation technologies. Net present value calculations were utilized to enable comparisons across time.The first analysis involved creating "proto-typical" wind plants of four different vintages, with commissioning years of 1999, 2003, 2008, and 2012. Proto-typical plants are representative of the industry at the time of their construction and rely on market data from each of the commissioning years to derive installation and equipment costs, power purchase agreement revenue, net capacity factor, receipt of federal production tax credit payments, and operation and maintenance expenses. For each of these four plants, future investment decisions to repower, or build a nearby greenfield site, were evaluated for 2015, 2020, 2025, and 2030.The second analysis focused on three actual wind plants operating in the United States. These plants were chosen for varying vintages and geographical diversity and include a Northeast wind plant (15-20 years old), a Midwest wind plant (10-15 years old), an...
The XML chip is now more than six years old. The diffusion of this technology has been very limited, due, on the one hand, to the long period of evolutionary development needed to develop hardware capable of accelerating a significant portion of the XML computing workload and, on the other hand, to the fact that the chip was invented by start-up Tarari in a commercial context which required, for business reasons, a minimum of public disclosure of its design features. It remains, nevertheless, a significant landmark that the XML chip has been sold and continuously improved for the last six years. From the perspective of general computing history, the XML chip is an uncommon example of a successful workload-specific symbolic computing device. With respect to the specific interests of the XML community, the XML chip is a remarkable validation of one of its core founding principles: normalizing on a data format, whatever its imperfections, would enable the developers to, eventually, create tools to process it efficiently. This paper was prepared for the International Symposium on Processing XML Efficiently: Overcoming Limits on Space, Time, or Bandwidth, a day of discussion among, predominately, software developers working in the area of efficient XML processing. The Symposium is being held as a workshop within Balisage, a conference of specialists in markup theory. Given the interests of the audience this paper does not delve into the design features and principles of the chip itself; rather it presents a dialectic on the motivation for the development of an XML chip in view of related and potentially competing developments in scaling as it is commonly characterized as a manifestation of Moore's Law, parallelization through increasing the number of computing cores on general purpose processors (multicore Von Neumann architecture), and optimization of software.
Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires significant amounts of pre-processing. More than the linguistic situation of Bambara itself, the socio-cultural context within which Bambara speakers live poses challenges for automated processing of this language. In this paper, we present the first parallel data set for machine translation of Bambara into and from English and French and the first benchmark results on machine translation to and from Bambara. We discuss challenges in working with low-resource languages and propose strategies to cope with data scarcity in low-resource machine translation (MT).
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