Design Methodology for Signal Processing

这项研究涉及信号处理系统异构实现的设计方法。一些信号处理算法由高度重复的、可预测的计算组成，这些计算是基于硬实时约束的样本流，并且适用于同步数据流表示和静态（编译时）调度。其他算法涉及一些决策或运行时控制流，并且可以被包含运行时流控制的更广泛的数据流模型有效地容纳。当这些算法组合成完整的系统时，必须支持更多的动态，并且必须包含离散事件原则或实时操作系统。这项研究正在寻找结合不同语义的方法，并正在追求联合模拟技术和理解每个计算模型中可用的形式化方法之间的相互作用。一个设计框架（称为托勒密），支持所有这些模型，单独或组合，正在开发作为一个试验台。

Lee This research deals with design methodology for heterogeneous implementations of signal processing systems. Some signal processing algorithms consist of highly repetitive, predictable computations on streams of samples with hard real- time constraints, and are amenable to synchronous dataflow representations and static (compile-time) scheduling. Other algorithms involve some decision making or run-time control flow, and can be accommodated efficiently by a broader dataflow model that includes run-time flow control. When such algorithms are combined into complete systems, more dynamics must be supported, and discrete-event principles, or real-time operating systems must be included. This research is looking at ways to combine diverse semantics, and is pursuing co-simulation techniques and understanding of the interaction between what formal methods are available within each model of computation. A design framework (called Ptolemy) that supports all these models, individually or in combination, is being developed as a testbed.