Ricardo Barrón Fernández

Automated Functional Test Generation for Digital Systems Through a Compact Binary Differential Evolution Algorithm

At present, the functional verification of a device represents the highest cost during manufacturing. To reduce that cost, several methods have been suggested. In this work we propose a method which produces a set of binary test sequences by means of a Compact binary Differential Evolution algorithm (Compact-BinDE). The strategy employed is based on the use of coverage models and cost functions in the verification process, which are built with relevant conditions or coverage points representing the full device behavior. The main problem is to cover all difficult situations since the relationships between the test points and the input data in the design are not trivial. The test generation method is included with a proposed verification platform based on a simulation representing a hybrid method. The main contribution of this work is that the method obtains test vector sequences that maximize the coverage percentage on run-time device simulation with an efficient search in the binary domain. Also, different to the previous works that used meta-heuristics, the proposed method by means of the Compact-BinDE algorithm can reduce the simulation time used to obtain test sequences that exercise the coverage points. The results show that the proposed method represents a good alternative to generate test sequences to cover the coverage points during the functional verification.

Automated Functional Coverage for a Digital System Based on a Binary Differential Evolution Algorithm

Within functional verification of digital systems there are dynamic methods based on Device Under Verification simulation. We focus on this type of method using functional coverage points. Nowadays, the main problem consists in obtaining high values to exercise all functional coverage points in the device. In this paper we propose a heuristic dynamic verification method based on a Binary Differential Evolution algorithm to obtain sets of vectors that maximize the functional coverage percentage in a synchronous First Input-First Output (FIFO) memory. The experimental results show that using this evolutionary technique with a relatively small population size, high functional coverage values were obtained. Despite the difficulty in exercising a greater amount of coverage points, we observed that the method obtains higher values than ninety percent in different scenarios.