Efficient and effective testing for simulation-based hardware verification is
challenging. Using constrained random test generation, several millions of
tests may be required to achieve coverage goals. The vast majority of tests do
not contribute to coverage progress, yet they consume verification resources.
In this paper, we propose a hybrid intelligent testing approach combining two
methods that have previously been treated separately, namely Coverage-Directed
Test Selection and Novelty-Driven Verification. Coverage-Directed Test
Selection learns from coverage feedback to bias testing toward the most
effective tests. Novelty-Driven Verification learns to identify and simulate
stimuli that differ from previous stimuli, thereby reducing the number of
simulations and increasing testing efficiency. We discuss the strengths and
limitations of each method, and we show how our approach addresses each
method's limitations, leading to hardware testing that is both efficient and
effective.

本文中，我们提出了一种混合智能测试方法，结合了 “覆盖向导测试选择” 和 “新颖性驱动验证” 两种方法，以实现高效有效的硬件测试。

仿真验证中的混合智能测试

Hybrid Intelligent Testing in Simulation-Based Verification

Constrained random test generation is one of the most widely adopted methods
for generating stimuli for simulation-based verification. Randomness leads to
test diversity, but tests tend to repeatedly exercise the same design logic.
Constraints are written (typically manually) to bias random tests towards
interesting, hard-to-reach, and yet-untested logic. However, as verification
progresses, most constrained random tests yield little to no effect on
functional coverage. If stimuli generation consumes significantly less
resources than simulation, then a better approach involves randomly generating
a large number of tests, selecting the most effective subset, and only
simulating that subset. In this paper, we introduce a novel method for
automatic constraint extraction and test selection. This method, which we call
coverage-directed test selection, is based on supervised learning from coverage
feedback. Our method biases selection towards tests that have a high
probability of increasing functional coverage, and prioritises them for
simulation. We show how coverage-directed test selection can reduce manual
constraint writing, prioritise effective tests, reduce verification resource
consumption, and accelerate coverage closure on a large, real-life industrial
hardware design.

本文介绍一种名为基于覆盖率反馈的测试选择的自动约束提取和测试选择的新方法，通过选择具有高概率增加功能覆盖率的测试，有助于减少手动约束编写，优先考虑有效测试，降低验证资源消耗，加速大型工业硬件设计的覆盖率闭合。