Search

This video is a mov video with a run time of 3 min. 39 sec.; color, with sound.

This video is a mov video with a run time of 3 min. 41 sec.; in color; with sound.

Design rule checking (DRC) is getting increasingly complex in advanced nodes technologies. It would be highly desirable to have a fast interactive DRC engine that could be used during layout. In this work, we establish the proof of feasibility for such an engine. The proposed model consists of a convolutional neural network (CNN) trained to detect DRC violations. The model was trained with artificial data that was derived from a set of $50$ SRAM designs. The focus in this demonstration was metal 1 rules. Using this solution, we can detect multiple DRC violations 32x faster than Boolean checkers with an accuracy of up to 92. The proposed solution can be easily expanded to a complete rule set.

Biconformal gravity, based on gauging of the conformal group to 2n dimensions, reproduces n-dim scale-covariant general relativity on the co-tangent bundle in any dimension. We generalize this result to include Yang-Mills matter sources formulated as SU\left(N\right) gauge theories with a twisted action on the full 2n-dimensional biconformal space. We show that the coupling of the sources to gravity does not stop the reduction of effective dimension 2n\rightarrow n of the gravity theory, and instead forces the Yang-Mills source to reduce to ordinary n-dimensional Yang-Mills theory on the gravitating cotangent bundle, with the usual Yang-Mills energy tensor as gravitational source. The results apply as well to gravity with sources on K\"ahler manifolds and in double field theories., Comment: 67 pages including appendices and references Clarified discussion of Kahler and Killing forms; added discussion of alternative Weyl gravity sources

FinFETs are predicted to advance semiconductorscaling for sub-20nm devices. In order to support their intro-duction into research and universities it is crucial to develop anopen source predictive process design kit. This paper discussesin detail the design process for such a kit for 15nm FinFETdevices, called the FreePDK15. The kit consists of a layerstack with thirteen-metal layers based on hierarchical-scalingused in ASIC architecture, Middle-of-Line local interconnectlayers and a set of Front-End-of-Line layers. The physical andgeometrical properties of these layers are defined and theseproperties determine the density and parasitics of the design. Thedesign rules are laid down considering additional guidelines forprocess variability, challenges involved in FinFET fabrication anda unique set of design rules are developed for critical dimensions.Layout extraction including modified rules for determining thegeometrical characteristics of FinFET layouts are implementedand discussed to obtain successful Layout Versus Schematicchecks for a set of layouts. Moreover, additional parasiticcomponents of a standard FinFET device are analyzed andthe parasitic extraction of sample layouts is performed. Theseextraction results are then compared and assessed against thevalidation models.

Vectors of data are at the heart of machine learning and data mining. Recently, vector quantization methods have shown great promise in reducing both the time and space costs of operating on vectors. We introduce a vector quantization algorithm that can compress vectors over 12x faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10x. Because it can encode over 2GB of vectors per second, it makes vector quantization cheap enough to employ in many more circumstances. For example, using our technique to compute approximate dot products in a nested loop can multiply matrices faster than a state-of-the-art BLAS implementation, even when our algorithm must first compress the matrices. In addition to showing the above speedups, we demonstrate that our approach can accelerate nearest neighbor search and maximum inner product search by over 100x compared to floating point operations and up to 10x compared to other vector quantization methods. Our approximate Euclidean distance and dot product computations are not only faster than those of related algorithms with slower encodings, but also faster than Hamming distance computations, which have direct hardware support on the tested platforms. We also assess the errors of our algorithm's approximate distances and dot products, and find that it is competitive with existing, slower vector quantization algorithms., Comment: Research track paper at KDD 2017