Abstract-This paper presents the modeling of embedded systems with SimBed, an execution-driven simulation testbed that measures the execution behavior and power consumption of embedded applications and RTOSs by executing them on an accurate architectural model of a microcontroller with simulated real-time stimuli. We briefly describe the simulation environment and present a study that compares three RTOSs: C/OS-II, a popular public-domain embedded real-time operating system; Echidna, a sophisticated, industrial-strength (commercial) RTOS; and NOS, a bare-bones multirate task scheduler reminiscent of typical "roll-your-own" RTOSs found in many commercial embedded systems. The microcontroller simulated in this study is the Motorola M-CORE processor: a low-power, 32-bit CPU core with 16-bit instructions, running at 20MHz. Our simulations show what happens when RTOSs are pushed beyond their limits and they depict situations in which unexpected interrupts or unaccounted-for task invocations disrupt timing, even when the CPU is lightly loaded. In general, there appears no clear winner in timing accuracy between preemptive systems and cooperative systems. The power-consumption measurements show that RTOS overhead is a factor of two to four higher than it needs to be, compared to the energy consumption of the minimal scheduler. In addition, poorly designed idle loops can cause the system to double its energy consumption-energy that could be saved by a simple hardware sleep mechanism.
The growing complexity of embedded applications and pressure on time-to-market has resulted in the increasing use of embedded real-time operating systems. Unfortunately, RTOSes can introduce a significant performance degradation. This paper presents the Real-Time Task Manager (RTM)-a processor extension that minimizes the performance drawbacks associated with RTOSes. The RTM accomplishes this by supporting, in hardware, a few of the common RTOS operations that are performance bottlenecks: task scheduling, time management, and event management. By exploiting the inherent parallelism of these operations, the RTM completes them in constant time, thereby significantly reducing RTOS overhead. It decreases both the processor time used by the RTOS and the maximum response time by an order of magnitude.
This paper presents the modeling of embedded systems with SimBed, an execution-driven simulation testbed that measures the execution behavior and power consumption of embedded applications and RTOSs by executing them on an accurate architectural model of a microcontroller with simulated real-time stimuli. We briefly describe the simulation environment and present a study that compares three RTOSs: µC/OS-II, a popular public-domain embedded real-time operating system; Echidna, a sophisticated, industrial-strength (commercial) RTOS; and NOS, a bare-bones multi-rate task scheduler reminiscent of typical "roll-your-own" RTOSs found in many commercial embedded systems. The microcontroller simulated in this study is the Motorola M-CORE processor: a low-power, 32-bit CPU core with 16-bit instructions, running at 20MHz.
This paper presents the modeling of embedded systems with SimBed, an execution-driven simulation testbed that measures the execution behavior and power consumption of embedded applications and RTOSs by executing them on an accurate architectural model of a microcontroller with simulated real-time stimuli. We briefly describe the simulation environment and present a study that compares three RTOSs: µC/OS-II, a popular public-domain embedded real-time operating system; Echidna, a sophisticated, industrial-strength (commercial) RTOS; and NOS, a bare-bones multi-rate task scheduler reminiscent of typical "roll-your-own" RTOSs found in many commercial embedded systems. The microcontroller simulated in this study is the Motorola M-CORE processor: a low-power, 32-bit CPU core with 16-bit instructions, running at 20MHz. 203 2 obtained by either instrumenting code or using off-chip memory and a logic analyzer, but both schemes would change the system's execution time and energy consumption.
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