VLSI circuits in nanometer VLSI technology experience significant variations -
intrinsic process variations and variations brought about by transistor degradation or
aging. These are generally embodied by yield loss or performance degradation over
operation time. Although the degradation can be compensated by the worst-case scenario
based over-design approach, it induces remarkable power overhead which is undesirable
in tightly power-constrained designs. Dynamic voltage scaling (DVS) is a more powerefficient
approach. However, its coarse granularity implies difficulty in handling finegrained
variations. These factors have contributed to the growing interest in poweraware
robust circuit design.
In this thesis, we propose a Built-In Proactive Tuning (BIPT) system, a lowpower
typical case design methodology based on dynamic prediction and prevention of
possible circuit timing errors. BIPT makes use of the canary circuit to predict the
variation induced performance degradation. The approach presented allows each circuit
block to autonomously tune its performance according to its own degree of variation.
The tuning is conducted offline, either at power on or periodically. A test pattern generator is included to reduce the uncertainty of the aging prediction due to different
input vectors.
The BIPT system is validated through SPICE simulations on benchmark circuits
with consideration of process variations and NBTI, a static stress based PMOS aging
effect. The experimental results indicate that to achieve the same variation resilience,
proposed BIPT system leads to 33% power savings in case of process variations as
compared to the over-design approach. In the case of aging resilience, the approach
proposed in this thesis leads to 40% less power than the approach of over-design while
30% less power as compared to DVS with NBTI effect modeling.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2891 |
| Date | 15 May 2009 |
| Creators | Shah, Nimay Shamik |
| Contributors | Hu, Jiang |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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