Design and Characterization of a Digital Delay Locked Loop Synthesized from Black Box Standard Cells

A dynamic method for efficient random mismatch characterization of standard cells

Proceedings of the International Conference on Computer-Aided Design - ICCAD '12 ◽

10.1145/2429384.2429419 ◽

2012 ◽

Cited By ~ 3

Author(s):

Wangyang Zhang ◽

Amith Singhee ◽

Jinjun Xiong ◽

Peter Habitz ◽

Amol Joshi ◽

...

Keyword(s):

Dynamic Method ◽

Standard Cells

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An In-depth and Black-box Characterization of the Effects of Clock Glitches on 8-bit MCUs

2011 Workshop on Fault Diagnosis and Tolerance in Cryptography ◽

10.1109/fdtc.2011.9 ◽

2011 ◽

Cited By ~ 55

Author(s):

Josep Balasch ◽

Benedikt Gierlichs ◽

Ingrid Verbauwhede

Keyword(s):

Black Box

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New voltage and temperature scalable gate delay model applied to a 14nm technology

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i3.pp1210-1220 ◽

2020 ◽

Vol 20 (3) ◽

pp. 1210

Author(s):

Kenza Charafeddine ◽

Faissal Ouardi

Keyword(s):

Classical Method ◽

Processing Unit ◽

Gate Delay ◽

Standard Cell ◽

Delay Model ◽

Standard Cell Library ◽

Central Processing ◽

Cell Library ◽

Standard Cells

The following work shows an innovative approach to model the timing of standard cells. By using mathematical models instead of the classical SPICE-based characterization, a high amount of CPU (Central Processing Unit) cores is saved and less amount of data is stored. In the present work, characterization of cells of a standard cell library is done in an hour whereas it is done in 650 hours with the classical method. Also, a method for validating and verification of the precision of the modelled data is presented by comparing them on a implemented circuit. The output of implementations shows less than 3% of error between the two methods.

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Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space

Evolutionary Computation ◽

10.1162/evco_a_00194 ◽

2017 ◽

Vol 25 (4) ◽

pp. 529-554 ◽

Cited By ~ 15

Author(s):

Mario A. Muñoz ◽

Kate A. Smith-Miles

Keyword(s):

Objective Assessment ◽

Black Box ◽

Accurate Estimation ◽

Problem Structure ◽

Algorithm Performance ◽

Hard Problems ◽

The Common ◽

Small Change ◽

Instance Space

This article presents a method for the objective assessment of an algorithm’s strengths and weaknesses. Instead of examining the performance of only one or more algorithms on a benchmark set, or generating custom problems that maximize the performance difference between two algorithms, our method quantifies both the nature of the test instances and the algorithm performance. Our aim is to gather information about possible phase transitions in performance, that is, the points in which a small change in problem structure produces algorithm failure. The method is based on the accurate estimation and characterization of the algorithm footprints, that is, the regions of instance space in which good or exceptional performance is expected from an algorithm. A footprint can be estimated for each algorithm and for the overall portfolio. Therefore, we select a set of features to generate a common instance space, which we validate by constructing a sufficiently accurate prediction model. We characterize the footprints by their area and density. Our method identifies complementary performance between algorithms, quantifies the common features of hard problems, and locates regions where a phase transition may lie.

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