This paper proposes an extension to dataflow models with timing specifications to facilitate the construction of deterministic, platform independent, precisely timed models of software in cyber-physical systems (CPS). Dataflow models are often used to describe the software/cyber part of a CPS, owing to their succinct and analyzable representation of computation and concurrency. To capture the interaction of the cyber with the physical part, it is common practice to augment the dataflow model with nodes to represent physical sensors and actuators and handle the timing outside the dataflow model. However, the precise timing of these interactions is critical to the overall application behavior, and conventional dataflow models do not capture these timing requirements. In this work, we introduce timing configurations in dataflow models to specify when this communication between cyber and physical parts takes place. Timing specifications are derived from application requirements which are independent of the platform execution behavior. A correct implementation must fulfill the dataflow and timing requirements. This paper discusses the extension of the well-studied Synchronous Dataflow (SDF) model with timing configurations, shows how traditional SDF analysis for consistency and deadlock freedom is adapted for this model, and discusses hierarchical composition and analysis of composite SDF nodes with timing configurations. We believe that a model for the cyber part of a CPS must allow for the specification of application timing behavior as an integral part of the model. Timing extensions for dataflow models accomplish this in a natural and comprehensible manner. By illustrating timing configurations for SDF, we lay the groundwork for their application to a variety of dataflow models.

Signal processing and multimedia applications are commonly modeled using Static/Cyclo-Static Dataflow (SDF/CSDF) models. SDF/CSDF explicitly specifies how much data is produced and consumed per firing during computation. This results in strong compile-time analyzability of many useful execution properties such as deadlock absence, channel boundedness, and throughput. However, SDF/CSDF is limited in its ability to capture how data is accessed in time. Hence, using these models often leads to implementations that are sub-optimal (i.e., use more resources than necessary) or even incorrect (i.e., use insufficient resources). In this work, we advance a new model called Static Dataflow with Access Patterns (SDF-AP) that captures the timing of data accesses (for both production and consumption). This paper formalizes the semantics of SDF-AP, defines key properties governing model execution, and discusses algorithms to check these properties under correctness and resource constraints. Results are presented to evaluate these analysis algorithms on practical applications modeled by SDF-AP.

FPGA devices provide flexible, fast, and low-cost prototyping and production solutions for system design. However, as the design complexity continues to rise, the design and synthesis iterations become a labor intensive and time consuming ordeal. Consequently, it becomes imperative to raise the level of abstraction for FPGA designs, while providing insight into performance metrics early in the design process. In particular, an important design time problem is to determine the maximum clock frequency that a circuit can achieve on a specific FPGA target before full synthesis and implementation. This early quantification can greatly help evaluate key design characteristics without reverting to tedious runs of the full implementation flow. In this work, we focus on the predictability of timing delay of circuits composed of high-level blocks on an FPGA. We are well aware of difficulties in tackling uncertainties in early timing estimation, e.g., an inherent gap between a high-level representation and gates/wires; extremely difficult delay estimation due to the randomness in physical design tools, etc. We show that the estimation uncertainties can be mitigated through a carefully characterized timing database of primitive building blocks and refined timing analysis models. We primarily focus on applications composed of data-intensive word-level arithmetic computations from the DSP domain and specified using static dataflow models. Our experiments indicate that for these applications, timing estimates can be obtained reliably within a good error margin on average and in the worst case. As future work, we plan to fine tune the timing database by modeling resource utilization effects and inter-primitive/actor routing delay via variants of Rent's rule and related efforts. We are also interested in exploring dynamic sub-cycle timing characterization.

Abstract Predictive analysis aims at detecting concurrency errors during runtime by monitoring a concrete execution trace of a concurrent program. In recent years, various models based on the happens-before causality relations have been proposed for predictive analysis. However, these models often rely on only the observed runtime events and typically do not utilize the program source code. Furthermore, the enumerative algorithms they use for verifying safety properties in the predicted traces often suffer from the interleaving explosion problem. In this paper, we introduce a precise predictive model based on both the program source code and the observed execution events, and propose a symbolic algorithm to check whether a safety property holds in all feasible permutations of events of the given trace. Rather than explicitly enumerating and checking the interleavings, our method conducts the search using a novel encoding and symbolic reasoning with a satisfiability modulo theory solver. We also propose a technique to bound the number of context switches allowed in the interleavings during the symbolic search, to further improve the scalability of the algorithm.