110 research outputs found
Enhancing Power Efficient Design Techniques in Deep Submicron Era
Excessive power dissipation has been one of the major bottlenecks for design and
manufacture in the past couple of decades. Power efficient design has become
more and more challenging when technology scales down to the deep submicron era
that features the dominance of leakage, the manufacture variation, the on-chip
temperature variation and higher reliability requirements, among others. Most of the computer aided design (CAD) tools and algorithms currently used in industry
were developed in the pre deep submicron era and did not consider the new features explicitly and adequately.
Recent research advances in deep submicron design, such as the mechanisms of leakage, the source and characterization of manufacture variation, the cause and
models of on-chip temperature variation, provide us the opportunity to incorporate these important issues in power efficient design. We explore this opportunity in this dissertation by demonstrating that significant power reduction can be achieved with only minor modification to the existing CAD tools and algorithms.
First, we consider peak current, which has become critical for circuit's reliability in deep submicron design. Traditional low power design techniques focus on
the reduction of average power. We propose to reduce peak current while keeping the overhead on average power as small as possible. Second, dual Vt technique and gate sizing have been used simultaneously for leakage savings. However, this approach becomes less effective in deep submicron design. We propose to use the newly developed process-induced mechanical stress to enhance its performance.
Finally, in deep submicron design, the impact of on-chip temperature variation on leakage and performance becomes more and more significant. We propose a temperature-aware dual Vt approach to alleviate hot spots and achieve further leakage reduction. We also consider this leakage-temperature dependency in the dynamic voltage scaling approach and discover that a commonly accepted result is incorrect for the current technology.
We conduct extensive experiments with popular design benchmarks, using the latest industry CAD tools and design libraries. The results show that our proposed enhancements are promising in power saving and are practical to solve the low power design challenges in deep submicron era
Berezinskii-Kosterlitz-Thouless transition of two-dimensional Bose gases in a synthetic magnetic field
We study the Berezinskii-Kosterlitz-Thouless transition of two-dimensional
Bose gases in a synthetic magnetic field using the standard Metropolis Monte
Carlo method. The system is described by the frustrated XY model and the
critical temperature is calculated though the absence of central peak of the
wave function in momentum space, which can be directly measured by the
time-of-flight absorbing imaging in cold atoms experiments. The results of our
work show agreement with former studies on superconducting Josephson arrays.Comment: 4 pages, 3 figure
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation
Transfer learning is a critical technique in training deep neural networks
for the challenging medical image segmentation task that requires enormous
resources. With the abundance of medical image data, many research institutions
release models trained on various datasets that can form a huge pool of
candidate source models to choose from. Hence, it's vital to estimate the
source models' transferability (i.e., the ability to generalize across
different downstream tasks) for proper and efficient model reuse. To make up
for its deficiency when applying transfer learning to medical image
segmentation, in this paper, we therefore propose a new Transferability
Estimation (TE) method. We first analyze the drawbacks of using the existing TE
algorithms for medical image segmentation and then design a source-free TE
framework that considers both class consistency and feature variety for better
estimation. Extensive experiments show that our method surpasses all current
algorithms for transferability estimation in medical image segmentation. Code
is available at https://github.com/EndoluminalSurgicalVision-IMR/CCFVComment: MICCAI2023(Early Accepted
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