Tartan: A General-Purpose Spatial Architecture for Exploiting Future Technologies

翻译后摘要：格子呢项目的目的是创建一个高性能的节能计算系统，通过了解空间/时间的权衡空间计算。 Tartan消除了伊萨的抽象，允许编译器传递管理应用程序执行所需的所有信息。 Tartan将完整的应用程序编译成特定于应用程序的电路。 这些电路可能相当大，并挑战商业CAD工具的可扩展性。 在这种情况下，Tartan利用基于全局关键路径的新技术，将优化器集中在电路的真正关键元件上。 这种新的优化方法适用于整个CAD社区。Tartan的底层计算基底是一个可重构的结构。 这种织物是规则的，因此适合用未来的纳米技术制造。 为了提高功率并确保可重新配置的结构容忍这种技术中的预期参数变化，使用无时钟自定时电路来实现结构。 用于在Tartan中实现这些电路的工具应该具有广泛的适用性，希望消除采用自定时电路的障碍之一。当今经济背后的驱动力之一是信息技术的增长，这本身就是由不断增加的可用性和不断降低的计算成本驱动的。 对格子图案的研究将有助于在未来继续这些趋势。空间计算有可能降低设计复杂性，提高性能，提高能源效率，并支持未来的纳米技术。

Abstract:The aim of the Tartan project is to create a high-performance energy-efficient computing system by understanding the space/time trade-off in spatial computing. Tartan eliminates the abstraction of the ISA allowing the compiler to communicate all information necessary to manage the execution of the application. Tartan compiles complete applications into application specific circuit. These circuits can be quite large and challenge the scalability of commercial CAD tools. In this context, Tartan harnesses new techniques based on the global critical path to focus the optimizer on the truly critical elements of the circuit. This new approach to optimization is applicable to the entire CAD community.The underlying computing substrate in Tartan is a reconfigurable fabric. This fabric is regular and thus amenable to manufacturing with future nanoscale technologies. To improve power and to ensure that the reconfigurable fabric is tolerant of the expected parametric variation in such technologies the fabric is implemented using clockless self-timed circuits. The tools used to implement these circuits in Tartan should have broad applicability, hopefully eliminating one the roadblocks towards the adoption of self-timed circuits.One of the driving forces behind today's economy is the growth of information technology, which itself is driven by the constantly increasing availability, and decreasing cost, of computing. Research into Tartan will help to continue these trends well into the future.Spatial computing has the potential to reduce design complexity, improve performance, improve energy efficiency, and support future nanoscale technologies.