Energy-efficient subthreshold processor design -

Energy-efficient subthreshold processor design

Rapid advances in digital circuit design has enabled a number of applications requiring complex sensornetworks. This application space ranges widely from environmental sensing to structural monitoring to supply chain management. Highly integrated sensor network platforms need to ombine MEMS sensing capabilities with digital processing and storage hardware, a low power radio,and an on-chip battery in a volume on the order of 1 mm .

The design of energy-efficient data processing and storage elements is therefore paramount.

In this paper , we present a highly efficient subthreshold microprocessor targeting sensorapplication. It is optimized across different design stages including ISA definition, microarchitecture evaluation and circuit and implementation optimization.

Our investigation concludes that microarchitectural decisions in the subthreshold regime differ significantly from that in conventional superthreshold mode. We propose a new general-purpose sensor processor architecture, which we call the Subliminal Processor.

On the circuit side, subthreshold operation is known to exhibit an optimal energy point. However, propagation delay also becomes more sensitive to process variation and can reduce the energy scaling gain. We conduct thorough analysis on how supply voltage and operating frequency impact energy efficiency in a statistical context.

With careful library cell selection and robust static RAM design, the Subliminal Processor operates correctly down to 200 mV in a 0.13 micron technology, which is sufficiently low to operate at Vmin . Silicon measurements of the Subliminal Processor show a maximum energy efficiency of 2.6 pJ/instruction at 360 mV supply voltage and 833 kHz operating frequency.

Finally, we examine the variation in frequency and across die to verify our analysisof adaptive tuning of the clock frequency and for optimal energy efficiency.

(***Other authors : Javin Olson, Anna Reeves, Michael Minuth, Ryan Helfand, Todd Austin, Dennis Sylvester, and David Blaauw, University of Michigan )

To read more of this external content, download the complete paper from the open online author archives at the University of Michigan.

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