Learning Semantic Representations to Verify Hardware Designs

Verification is a serious bottleneck in the industrial hardware design cycle, routinely requiring person-years of effort. Practical verification relies on a "best effort" process that simulates the design on test inputs. This suggests a new research question: Can this simulation data be exploited to learn a continuous representation of a hardware design that allows us to predict its functionality? As a first approach to this new problem, we introduce Design2Vec, a deep architecture that learns semantic abstractions of hardware designs. The key idea is to work at a higher level of abstraction than the gate or the bit level, namely the Register Transfer Level (RTL), which is similar to software source code, and can be represented by a graph that incorporates control and data flow. This allows us to learn representations of RTL syntax and semantics using a graph neural network. We apply these representations to several tasks within verification, including predicting what cover points of the design will be covered (simulated) by a test, and generating new tests to cover desired cover points. We evaluate Design2Vec on three real-world hardware designs, including the TPU, Google's industrial chip used in commercial data centers. Our results demonstrate that Design2Vec dramatically outperforms baseline approaches that do not incorporate the RTL semantics and scales to industrial designs. It generates tests that cover design points that are considered hard to cover with manually written tests by design verification experts in a fraction of the time.