No abstract

No abstract

Embedded streaming applications require design-time temporal analysis to verify real-time constraints such as throughput and latency. In this paper, we introduce a new analytical technique to compute temporal bounds of streaming applications mapped onto a shared multiprocessor platform. We use an expressively rich application model that supports adaptive applications where graph structure, execution times and data rates may change dynamically. The analysis technique combines symbolic simulation in (max, +) algebra with worst-case resource availability curves. It further enables a tighter performance guarantee by improving the WCRTs of service requests that arrive in the same busy time. Evaluation on real-life application graphs shows that the technique is tens of times faster than the state-of-the-art and enables tighter throughput guarantees, up to a factor of 4, compared to the typical worst-case analysis.

No abstract

Abstract-A present-day System-on-Chip (SoC) runs a wide range of applications with diverse real-time requirements. Resources, such as processors, interconnects and memories, are shared between these applications to reduce cost. Resource sharing causes temporal interference, which must be bounded by a suitable resource arbiter. System-level analysis techniques use the service guarantee of the arbiter to ensure that realtime requirements of these applications are satisfied. A service guarantee that underestimates the minimum service provided by an arbiter results in more allocation of resources than needed to satisfy latency and throughput requirements. For instance, a linear service guarantee cannot accurately capture bursty service provision by many priority-based schedulers, such as Credit-Controlled Static Priority (CCSP) and Priority-Budget Scheduling (PBS). As a result, the timing analysis of these arbiters becomes too pessimistic. This leads to unnecessary cost penalties since some SoC resources, such as SDRAM bandwidth, are scarce and expensive.