Automation of Processor Verification Using Recurrent Neural Networks

TL;DR

This paper introduces a novel method that uses recurrent neural networks to dynamically adjust pseudorandom stimuli generation, significantly improving processor verification coverage and efficiency.

Contribution

The paper presents a new technique integrating RNNs with coverage feedback to enhance simulation-based processor verification processes.

Findings

Coverage closure achieved faster

Small high-coverage stimulus set isolated

Applicable to various processor types

Abstract

When considering simulation-based verification of processors, the current trend is to generate stimuli using pseudorandom generators (PRGs), apply them to the processor inputs and monitor the achieved coverage of its functionality in order to determine verification completeness. Stimuli can have different forms, for example, they can be represented by bit vectors applied to the input ports of the processor or by programs that are loaded directly into the program memory. In this paper, we propose a new technique dynamically altering constraints for PRG via recurrent neural network, which receives a coverage feedback from the simulation of design under verification. For the demonstration purposes we used processors provided by Codasip as their coverage state space is reasonably big and differs for various kinds of processors. Nevertheless, techniques presented in this paper are widely applicable. The results of experiments show that not only the coverage closure is achieved much sooner, but we are able to isolate a small set of stimuli with high coverage that can be used for running regression tests.