scispace - formally typeset
Search or ask a question
Institution

Cadence Design Systems

CompanySan Jose, California, United States
About: Cadence Design Systems is a company organization based out in San Jose, California, United States. It is known for research contribution in the topics: Circuit design & Routing (electronic design automation). The organization has 3139 authors who have published 3745 publications receiving 66410 citations. The organization is also known as: Cadence Design Systems, Inc.


Papers
More filters
Proceedings ArticleDOI
08 Jun 2008
TL;DR: This paper proposes a new statistical regression technique that applies a novel strategy to address the high dimensionality issue of continuous technology scaling and proposes a novel recursive estimator to accurately and efficiently predict the moment values.
Abstract: The continuous technology scaling brings about high-dimensional performance variations that cannot be easily captured by the traditional response surface modeling. In this paper we propose a new statistical regression (STAR) technique that applies a novel strategy to address this high dimensionality issue. Unlike most traditional response surface modeling techniques that solve model coefficients from over-determined linear equations, STAR determines all unknown coefficients by moment matching. As such, a large number of (e.g., 103~105) model coefficients can be extracted from a small number of (e.g., 102~103) sampling points without over-fitting. In addition, a novel recursive estimator is proposed to accurately and efficiently predict the moment values. The proposed recursive estimator is facilitated by exploiting the interaction between different moment estimators and formulating the moment estimation problem into a special form that can be iteratively solved. Several circuit examples designed in commercial CMOS processes demonstrate that STAR achieves more than 20times runtime speedup compared with the traditional response surface modeling.

40 citations

Proceedings ArticleDOI
10 Jun 2002
TL;DR: A novel method of generating a gray box timing model from gate-level netlist by reducing a timing graph is proposed, which provides model accuracy including arbitrary levels of latch time borrowing and capability to support timing constraints that span multiple blocks.
Abstract: A timing model extractor builds a timing model of a digital circuit for use with a static timing analyzer. This paper proposes a novel method of generating a gray box timing model from a gate-level netlist by reducing a timing graph. Previous methods of generating timing models sacrificed accuracy and/or did not scale well with design size. The proposed method is simple, yet it provides model accuracy including arbitrary levels of latch time borrowing and the capability to support timing constraints that span multiple blocks. Also, cpu and memory resources required to generate the model scale well with size of the circuit. The generated model can provide a capacity improvement in timing verification by more than two orders of magnitude.

40 citations

Proceedings ArticleDOI
23 May 2005
TL;DR: This work presents a hardware-efficient design increasing throughput for the AES algorithm using a high-speed parallel pipelined architecture, and achieves a high throughput of 29.77 Gbit/s in encryption whereas the highest throughput reported in the literature is 21.54 G bit/s.
Abstract: In November 2001, the National Institute of Standards and Technology (NIST) of the USA chose the Rijndael algorithm as the suitable Advanced Encryption Standard (AES) to replace the Data Encryption Standard (DES) algorithm Since then, many hardware implementations have been proposed in literature We present a hardware-efficient design increasing throughput for the AES algorithm using a high-speed parallel pipelined architecture By using an efficient inter-round and intra-round pipeline design, our implementation achieves a high throughput of 2977 Gbit/s in encryption whereas the highest throughput reported in the literature is 2154 Gbit/s

40 citations

Journal ArticleDOI
TL;DR: Experimental results suggest that the algorithm dramatically outperforms the only existing implicit method which must compute the transitive closure of the adjacency-matrix of the graphs and applies it to solve the bad cycle detection problem encountered in formal verification.
Abstract: This paper first presents a binary decision diagram-based implicit algorithm to compute all maximal strongly connected components (SCCs) of directed graphs. The algorithm iteratively applies reachability analysis and sequentially identifies SCCs. Experimental results suggest that the algorithm dramatically outperforms the only existing implicit method which must compute the transitive closure of the adjacency-matrix of the graphs. This paper then applies this SCC algorithm to solve the bad cycle detection problem encountered in formal verification. Experimental results show that our new bad cycle detection algorithm is typically significantly faster than the state-of-the-art, sometimes by more than a factor of ten.

40 citations

Patent
23 Dec 2002
TL;DR: In this article, the authors use non-orthogonal lines as cut lines that divide the IC layout into regions, and then generate congestion-cost estimates by measuring the number of nets cut by the cut lines.
Abstract: Some embodiments of the invention are placers that use lines that are not orthogonal with each other to calculate the costs of potential placement configurations. Some of these embodiments use non-orthogonal lines to measure congestion costs of potential placement configurations. For instance, some embodiments use non-orthogonal lines as cut lines that divide the IC layout into regions. These embodiments then generate congestion-cost estimates by measuring the number of nets cut by the cut lines.

40 citations


Authors

Showing all 3142 results

NameH-indexPapersCitations
Alberto Sangiovanni-Vincentelli9993445201
Derong Liu7760819399
Andrew B. Kahng7661824097
Jason Cong7659424773
Kenneth L. McMillan6015020835
Edoardo Charbon6052612293
Richard B. Fair5920514653
John P. Hayes5830211206
Sachin S. Sapatnekar5642412543
Wayne G. Paprosky5619610571
Robert G. Meyer4911613011
Scott M. Sporer491508085
Charles J. Alpert492248287
Joao Marques-Silva482899374
Paulo Flores483217617
Network Information
Related Institutions (5)
Intel
68.8K papers, 1.6M citations

90% related

Qualcomm
38.4K papers, 804.6K citations

87% related

Motorola
38.2K papers, 968.7K citations

84% related

Samsung
163.6K papers, 2M citations

83% related

Hewlett-Packard
59.8K papers, 1.4M citations

82% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20223
2021103
2020185
2019212
2018103
201788