Exploiting X-correlation in output compression via superset X-canceling

An alternative to masking unknown (X) values before the compactor (i.e., X-masking) is to capture X's in the MISR and cancel them out after compaction (i.e., X-canceling). Existing X-canceling methodologies require a number of control bits to perform the X-canceling that is linear in the number...

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description An alternative to masking unknown (X) values before the compactor (i.e., X-masking) is to capture X's in the MISR and cancel them out after compaction (i.e., X-canceling). Existing X-canceling methodologies require a number of control bits to perform the X-canceling that is linear in the number of X's to be canceled. This paper describes a new methodology for X-canceling which can exploit the fact that the scan cells in which X's are captured tend to be highly correlated in order to significantly reduce the number of control bits required for X-canceling. X's tend to be generated in certain portions of the design, and hence certain scan cells capture X's with much higher frequency than other scan cells. Instead of custom generating the control bits to cancel out only the X's in one MISR signature, the proposed approach finds a general superset solution which can cancel out the X's for many MISR signatures. This allows the same control bits to be reused many times thereby significantly improving the amount of compression that can be obtained. Architectures for implementing superset X-canceling are described along with experimental results.
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subjects Equations
Indexes
Mathematical model
Merging
Random access memory
System-on-a-chip
Vectors
title Exploiting X-correlation in output compression via superset X-canceling
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