Reliable systems for micro aerial vehicles — Adaptive clocking and future work

Editor's Note: Embedded designers must contend with a host of challenges in creating systems for harsh environments. Harsh environments present unique characteristics not only in terms of temperature extremes but also in areas including availability, security, very limited power budget, and more. In Rugged Embedded Systems, the authors present a series of papers by experts in each of the areas that can present unusually demanding requirements. A separate excerpt of the book addresses fundamental concerns in reliability and system resiliency.





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Adapted from Rugged Embedded Systems, Computing in Harsh Environments, by Augusto Vega. Pradip Bose, Alper Buyuktosunoglu.

CHAPTER 7. Reliable electrical systems for micro aerial vehicles and insect-scale robots: Challenges and progress (Cont.)
By X. Zhang, Washington University, St. Louis, MO, United States

5.4.3 Adaptive-frequency clocking with unregulated voltage

We now turn our attention to how adaptive-frequency clocking performs with an unregulated voltage generated by the SC-IVR operating in open loop. We used the same test setup with and noise injection via the on-chip generator. The failure rates and the average frequencies are captured in Fig. 15 . Compared to the measured results in Fig. 14B , average frequencies are much higher, because DVDD settles to higher values (≈0.8 V) when the SC-IVR operates in open loop. Despite the high susceptibility to fluctuations on DVDD to load current steps as seen in Figs. 10 and 15A shows zero errors occurred even for D1⁄410. The higher DVDD voltage provides more cushion to avoid intermittent retention failure.

In order to illustrate the extended operating range offered by running the SC-IVR in open loop, Fig. 15B plots the average DCO frequency and average DVDD voltage for error-free operation versus battery voltage. These measurements were again made with 0–15 mA current load steps. As expected, the open-loop SC-IVR’s average output voltage scales proportional to the battery voltage. Moreover, the system can operate error-free even for battery voltages below 3 V, which approaches the 2.5–2.7 V lower discharge limit of Li-ion batteries. In comparison, assuming a target SC-IVR regulated voltage of 0.7 V, the system would only operate down to a battery voltage of 3.2 V and at a lower frequency across the battery discharge profile even with the adaptive-frequency clocking scheme. A fixed-frequency clocking scheme would lead to even lower performance.

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FIG. 15 Performance under unregulated voltage from open-loop SC-IVR operation. (A) Failure rate and average frequency versus DCO settings and (B) measured average clock frequency and output voltage.

Finally, we look at the transient waveform of the supply voltage captured by the internal voltage monitor when the SC-IVR operates in open loop with the same periodic load step condition used in Fig. 15A . Fig. 16 shows both CLKOUT, which is a divide-by-2 signal of the internal system clock, and the supply voltage (DVDD). The DCO’s control code is set to 10 as suggested by Fig. 15A . In the zoom-in window of the waveforms, it clearly illustrates that the DCO frequency can respond promptly to a 82.1 mV supply droop within 6.82 ns, as the load current steps from 5 to 30 mA, so that no memory access error is recorded by the BIST module, suggesting superior supply-noise resilience of the SoC system due to the adaptive clocking scheme. Moreover, the waveform of the supply voltage demonstrates that supply noise in a typical microrobotic SoC with IVR is characterized more by the voltage droop and ripple in response to the load current steps and the IVR switching, instead of resonant noise cause by the LC tank in the power delivery path.

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FIG. 16 Waveforms of the supply voltage (DVDD) and a divide-by-2 clock signal (CLKOUT) during IVR open-loop operation with load current (ILOAD) periodically switching between 5 and 30 mA.

In addition to validating the resilience and the performance advantages of adaptive-frequency clocking, our experimental results also reveal the synergistic properties between the clocking scheme and the IVR design in a battery-powered microrobotic SoC. The supply-noise resilience provided by an adaptive clock alleviates design constraints imposed by voltage ripple and voltage droop. Therefore, the IVR can trade-off its transient response for better efficiency or smaller area when codesigned with adaptive-frequency clocking.






In this chapter, we have presented the design context and motivation of customized SoC for microrobotic application and have done an in-depth investigation of supply resilience in battery-powered microrobotic system using the prototype chip, which is the precursor of BrainSoC, with both analytical and experimental evaluations. The conclusion we are able to arrive at is that our proposed adaptive-frequency clocking scheme offers major advantages when combined with an IVR in a battery-powered microrobotic SoC. Our thorough evaluation has revealed the subtle but crucial tradeoffs and merits associated with different configurations of the IVR and the clock— for regulated voltage operation via closed-loop IVR, adaptive-frequency clocking enables 2 performance improvement, compared to conventional fixed-frequency clocking scheme; combining adaptive-frequency clocking with an unregulated voltage via open-loop IVR extends the operating range across a wider portion of the battery’s discharge profile.

Our study has mainly focused on the effect of battery discharge and voltage regulator operation on supply noise with the running of the digital processor. However, another important aspect of supply disturbance in the context of RoboBee is the interference from the piezoelectric actuator drive, which consumes significant portion of the system power by drawing large energy packet in periodic bursts. We plan to further investigate this effect in the mounted flight experiment with the fully on-board electronic system on the RoboBee.

In addition to supply resilience and electronics robustness, there are other aspects of a robot design that warrant careful considerations in order to improve the reliability of the overall system. And the system trade-off does not stop at the individual robot level. As we have hinted at the beginning of the chapter, one facet of reliability that is unique to microrobots stems from their capability to cooperate in swarms.

Unlike conventional large-scale robot, a microrobotic swarm can tolerate multiple failures from individual robots, yet still accomplish the mission at hand. Study [ 22 ] has found that degradation of hardware reliability can be tolerated to some extent without significant affect on the swarm performance. Moreover, harsh environmental factors, such as extreme temperature and severe electromagnetic field may present yet more restricted design specifications and additional reliability considerations.

Many challenges remain to be solved before we can unleash the full power of autonomous MAV, but one message is clear—it is immensely valuable to build microrobotic platforms such as RoboBee to provide a development environment and a research testbed for all the open scientific and engineering questions that beg to be answered. I hope you have had as much fun in learning about our journey towards an autonomous insect-scale flying robot as we had in building it.


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Reprinted with permission from Elsevier/Morgan Kaufmann, Copyright © 2016

Professor Zhang joined the faculty at Washington University in St. Louis in 2015. Previously, she was a postdoctoral fellow in computer science at Harvard University, where she worked on the RoboBee BrainSoC and energy-efficient computing projects. She has worked as a graduate research assistant at Cornell University studying variability-tolerant circuits. Zhang earned a doctorate in electrical and computer engineering at Cornell University in 2012. She earned a bachelor’s degree in electrical engineering at Tsinghua University in Beijing in 2006.

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