Improving polynomial datapath debugging with HEDs

Abstract

In this paper, we introduce a formal and scalable debugging approach to derive a reduced ordered set of design error candidates in polynomial datapath designs. To make our debugging method scalable for large designs, we utilize a Modular Horner Expansion Diagram (M-HED), which has been shown to be a scalable high level decision model. In our method, we extract data dependency graphs from the polynomial datapath designs using static slicing. Then we combine backward and forward path tracing to extract a reduced set of error candidates. In order to increase the accuracy of the method in the presence of multiple design errors, we rank the error candidates in decreasing order of their probability of being an error using a proposed priority criterion. In order to evaluate the effectiveness of our method, we have applied it to several large designs. The experimental results show that the proposed method enables us to locate even multiple errors with high accuracy in a short run time.