Reducing Frequency in Real-Time Systems via Speculation and Fall-Back Recovery

Abstract

In real-time systems, safe operation requires that tasks complete before their deadlines. Static worst-case timing analysis is used to derive an upper bound on the number of cycles for a task, and this is the basis for a safe frequency that ensures timely completion in any scenario. Unfortunately, it is difficult to tightly bound the number of cycles for a complex task executing on a complex pipeline, and so the safe frequency tends to be over-inflated. Power efficiency is sacrificed for safety.

The situation only worsens as advanced microarchitectural techniques are deployed in embedded systems. High-performance microarchitectural techniques such as caching, branch prediction, and pipelining decrease typical execution times. At the same time, it is difficult to tightly bound the worst-case execution time of complex tasks on highly dynamic substrates. As a result, the gap between worst-case execution time and typical execution time is expected to increase.

This thesis explores frequency speculation, a technique for reconciling the power/safety trade-off. Tight but unsafe bounds (derived from past task executions) are the basis for a low speculative frequency. The task is divided into multiple smaller sub-tasks and each sub-task is assigned an interim soft deadline, called a checkpoint. Sub-tasks are attempted at the speculative frequency. Continued safe progress of the task as a whole is confirmed for as long as speculative sub-tasks complete before their checkpoints. If a sub-task exceeds its checkpoint (misprediction), the system falls back to a higher recovery frequency that ensures the overall deadline is met in spite of the interim misprediction.

The primary contribution of this thesis is the development of two new frequency speculation algorithms. A drawback of the original frequency speculation algorithm is that a sub-task misprediction is detected only after completing the sub-task. The misprediction can be detected earlier through the use of a watchdog timer that expires at the checkpoint unless the sub-task completes in time to advance it to the next checkpoint. Early detection is superior because recovery can be initiated earlier, in the middle of the mispredicted sub-task. This introduces extra slack that can be used to lower the speculative frequency even further.

A new issue that arises with early detection is bounding the amount of work that remains in the mispredicted sub-task after the misprediction is detected. The two new algorithms differ in how the unfinished work is bounded. The first algorithm conservatively bounds the execution time of the unfinished portion using the worst-case execution time of the entire sub-task. The second uses more sophisticated analysis to derive a much tighter bound. Both early-detection algorithms outperform the late-detection algorithm. For tight deadlines, the sophisticated analysis of the second early-detection algorithm truly pays off. It yields 60-70% power savings for six real-time applications from the C-lab suite.