5,589 research outputs found
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical EDA problems suffer from the curse of dimensionality, i.e. the
very fast-scaling computational burden produced by large number of parameters
and/or unknown variables. This phenomenon may be caused by multiple spatial or
temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit
simulation), nonlinearity of devices and circuits, large number of design or
optimization parameters (e.g. full-chip routing/placement and circuit sizing),
or extensive process variations (e.g. variability/reliability analysis and
design for manufacturability). The computational challenges generated by such
high dimensional problems are generally hard to handle efficiently with
traditional EDA core algorithms that are based on matrix and vector
computation. This paper presents "tensor computation" as an alternative general
framework for the development of efficient EDA algorithms and tools. A tensor
is a high-dimensional generalization of a matrix and a vector, and is a natural
choice for both storing and solving efficiently high-dimensional EDA problems.
This paper gives a basic tutorial on tensors, demonstrates some recent examples
of EDA applications (e.g., nonlinear circuit modeling and high-dimensional
uncertainty quantification), and suggests further open EDA problems where the
use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and
System
Bridging the Gap between Physical and Circuit Analysis for Variability-Aware Microwave Design: Power Amplifier Design
Process Induced Variability (PIV) stemming from fabrication tolerance can impact the performance of integrated circuits. This issue is particularly significant at high frequencies, since Monolithic Microwave Integrated Circuits (MMICs) rely on advanced semiconductor technologies exploiting device sizes at the nanoscale in conjunction with complex passive structures, featuring both distributed elements (transmission lines) and lumped components. Black-box (behavioral) models extracted from accurate physical simulations can be profitably exploited to incorporate PIV into circuit-level MMIC analysis. In this paper, these models are applied to the statistical analysis of a single and of a combined MMIC power amplifier designed in GaAs technology for X-band applications. The relative impact of the active device variability towards the passive matching networks one is evaluated, demonstrating the relevance of PIV. The significant spread found, with only two variable parameters, confirms the importance of a PIV-aware PA design approach, with suitable margins and careful network optimization
On Timing Model Extraction and Hierarchical Statistical Timing Analysis
In this paper, we investigate the challenges to apply Statistical Static
Timing Analysis (SSTA) in hierarchical design flow, where modules supplied by
IP vendors are used to hide design details for IP protection and to reduce the
complexity of design and verification. For the three basic circuit types,
combinational, flip-flop-based and latch-controlled, we propose methods to
extract timing models which contain interfacing as well as compressed internal
constraints. Using these compact timing models the runtime of full-chip timing
analysis can be reduced, while circuit details from IP vendors are not exposed.
We also propose a method to reconstruct the correlation between modules during
full-chip timing analysis. This correlation can not be incorporated into timing
models because it depends on the layout of the corresponding modules in the
chip. In addition, we investigate how to apply the extracted timing models with
the reconstructed correlation to evaluate the performance of the complete
design. Experiments demonstrate that using the extracted timing models and
reconstructed correlation full-chip timing analysis can be several times faster
than applying the flattened circuit directly, while the accuracy of statistical
timing analysis is still well maintained
Current-Mode Techniques for the Implementation of Continuous- and Discrete-Time Cellular Neural Networks
This paper presents a unified, comprehensive approach
to the design of continuous-time (CT) and discrete-time
(DT) cellular neural networks (CNN) using CMOS current-mode
analog techniques. The net input signals are currents instead
of voltages as presented in previous approaches, thus avoiding
the need for current-to-voltage dedicated interfaces in image
processing tasks with photosensor devices. Outputs may be either
currents or voltages. Cell design relies on exploitation of current
mirror properties for the efficient implementation of both linear
and nonlinear analog operators. These cells are simpler and
easier to design than those found in previously reported CT
and DT-CNN devices. Basic design issues are covered, together
with discussions on the influence of nonidealities and advanced
circuit design issues as well as design for manufacturability
considerations associated with statistical analysis. Three prototypes
have been designed for l.6-pm n-well CMOS technologies.
One is discrete-time and can be reconfigured via local logic for
noise removal, feature extraction (borders and edges), shadow
detection, hole filling, and connected component detection (CCD)
on a rectangular grid with unity neighborhood radius. The other
two prototypes are continuous-time and fixed template: one for
CCD and other for noise removal. Experimental results are given
illustrating performance of these prototypes
Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control
Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control
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