An Error Model to Study the Behavior of Transient Errors in Sequential Circuits

An Error Model to Study the Behavior of Transient Errors in Sequential Circuits Lingasubramanian, K. Bhanja, S. Nano Comput. Res. Group (NCRG), Univ. of South Florida, Tampa, FL This paper appears in: VLSI Design, 2009 22nd International Conference on Publication Date: 5-9 Jan. 2009 On page(s): 485 - 490 Location: New Delhi ISSN: 1063-9667 ISBN: 978-0-7695-3506-7 Digital Object Identifier: 10.1109/VLSI.Design.2009.73 Current Version Published: 2009-01-19

Abstract In sequential logic circuits the transient errors that occur in a particular time frame will propagate to consecutive time frames thereby making the device more vulnerable. In this work we propose a probabilistic error model for sequential logic that can measure the expected output error probability, given a probabilistic input space, that account for both spatial dependencies and temporal correlations across the logic, using a time evolving causal network. We demonstrate our error model using MCNC and ISCAS benchmark circuits and validate it with HSpice simulations. Our observations show that, significantly low individual gate error probabilities produce at least 5 fold higher output error probabilities. The average error percentage of our results with reference to HSpice simulation results is only 4.43%. Our observations show that the order of temporal dependency of error varies for different sequential circuits.

Direct Quadratic Minimization Using Magnetic Field-Based Computing

Direct Quadratic Minimization Using Magnetic Field-Based Computing Sarkar, S. Bhanja, S. Dept. of Comput. Sci. & Eng., South Univ., Tampa, FL This paper appears in: Design and Test of Nano Devices, Circuits and Systems, 2008 IEEE International Workshop on Publication Date: 29-30 Sept. 2008 On page(s): 31 - 34 Location: Cambridge, MA ISBN: 978-0-7695-3379-7 Digital Object Identifier: 10.1109/NDCS.2008.13 Current Version Published: 2008-10-03

Abstract We explore an unconventional front in computing,which we call magnetic field-based computing (MFC), that harnesses the energy minimization aspects of a collection of nanomagnets to solve directly quadratic energy minimization problems, such as those arising in computationaolly intensive computer vision tasks. The Hamiltonian of a collection of bipolar nanomagnets is governed by the pairwise dipolar interactions.The ground state of a nanomagnet collection minimizes this Hamiltonian. We have devised a computational method, based on multi-dimensional scaling, to decide upon the spatial arrangement of nanomagnets that matches a particular quadratic minimization problem. Each variable is represented by a nanomagnet and the distances between them are such that the dipolar interactions match the corresponding pairwise energy term in the original optimization problem. We select the nanomagnets that participate in a specific computation from a field of regularly placed nanomagnets. The nanomagnets that do not participate are deselected using transverse magnetic fields. We demonstrate these ideas by solving Landau-Lifshitz equations as implemented in the NISTpsilas micro-magnetic OOMMF software.

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Technical Program Co-chair

Dr. Bhanja was selected as Technical Program Co-Chair ACM Great Lakes Symposium on VLSI, 2008

Outstanding Faculty Researcher award

Dr. Bhanja receives University of South Florida “Outstanding Faculty Research Achievement Award”, 2008.

PASI

Javier Pulecio receives Pan American Science Institute scholarship

DSRC Invited Speaker

Dr. Bhanja delivers Invited talk sponsored by DSRC workshop held at Stanford University.

Sloan Fellowship

Javier Pulecio receives Alfred P Sloan fellowship