Automatic Transpiler that Efficiently Converts Digital Circuits to a Neural Network Representation

Abstract

Digital circuits are the basic structure of most of today's electronic devices. Simulation plays a critical role in the iterative development of such circuits as a consequence of the time and financial expenses that accompany fabrication. There are existing tools that achieve simulation by modeling circuits with Hardware Description Languages (HDL). However, with recent advances in neural networks (NN) and hardware accelerators for NN simulation, a niche of digital circuit simulation via NNs has opened up. Here, we introduce C2NN (Circuit to Neural Network), a novel method that converts (or transpiles) any digital circuit expressed in a HDL into a NN for simulation. The conversion to a NN representation not only affords the benefits of parallelization, the use of GPUs for simulation, and optimizations such as pruning, but it also provides a methodology for achieving equivalent digital circuit computation in neuromorphic hardware. We describe the transpilation process of C2NN and verify its correctness on small- and large-scale digital circuits. We also found that the simulation time of the transpiled circuits is competitive with one of the fastest digital circuit simulators.