# Mixed-level identification of fault redundancy in microprocessors

**Authors:** Adeboye Stephen Oyeniran, Raimund Ubar, Maksim Jenihhin, Cemil Cem, Gursoy, Jaan Raik

arXiv: 1907.12325 · 2019-07-30

## TL;DR

This paper introduces a high-level functional fault model for control faults in microprocessors, along with methods for test data generation, fault coverage evaluation, and redundancy identification, supported by experimental results.

## Contribution

It presents a novel high-level fault model based on instruction sets and data constraints, enabling efficient test generation and redundancy detection in microprocessors.

## Key findings

- High-level test guarantees detection of all non-redundant low-level faults.
- Test coverage of high-level faults correlates with gate-level SAF coverage.
- Experimental results validate the effectiveness of the proposed methods.

## Abstract

A new high-level implementation independent functional fault model for control faults in microprocessors is introduced. The fault model is based on the instruction set, and is specified as a set of data constraints to be satisfied by test data generation. We show that the high-level test, which satisfies these data constraints, will be sufficient to guarantee the detection of all non-redundant low level faults. The paper proposes a simple and fast simulation based method of generating test data, which satisfy the constraints prescribed by the proposed fault model, and a method of evaluating the high-level control fault coverage for the proposed fault model and for the given test. A method is presented for identification of the high-level redundant faults, and it is shown that a test, which provides 100% coverage of non-redundant high-level faults, will also guarantee 100% non-redundant SAF coverage, whereas all gate-level SAF not covered by the test are identified as redundant. Experimental results of test generation for the execution part of a microprocessor support the results presented in the paper.

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Source: https://tomesphere.com/paper/1907.12325