DEMOTIC: A Differentiable Sampler for Multi-Level Digital Circuits

TL;DR

Demotic introduces a differentiable, GPU-accelerated sampler for CircuitSAT problems that significantly outperforms heuristic methods in runtime, enabling efficient sampling of digital circuit solutions.

Contribution

It presents Demotic, a novel gradient descent-based differentiable sampler for multi-level digital circuits, improving scalability and runtime over existing heuristic approaches.

Findings

Demotic achieves over 100x faster sampling times on ISCAS-85 benchmarks.

It effectively learns valid solutions using gradient descent as a supervised regression task.

The approach enables parallel, GPU-accelerated sampling for complex digital circuits.

Abstract

Efficient sampling of satisfying formulas for circuit satisfiability (CircuitSAT), a well-known NP-complete problem, is essential in modern front-end applications for thorough testing and verification of digital circuits. Generating such samples is a hard computational problem due to the inherent complexity of digital circuits, size of the search space, and resource constraints involved in the process. Addressing these challenges has prompted the development of specialized algorithms that heavily rely on heuristics. However, these heuristic-based approaches frequently encounter scalability issues when tasked with sampling from a larger number of solutions, primarily due to their sequential nature. Different from such heuristic algorithms, we propose a novel differentiable sampler for multi-level digital circuits, called {\sc Demotic}, that utilizes gradient descent (GD) to solve the CircuitSAT problem and obtain a wide range of valid and distinct solutions. {\sc Demotic} leverages the circuit structure of the problem instance to learn valid solutions using GD by re-framing the CircuitSAT problem as a supervised multi-output regression task. This differentiable approach allows bit-wise operations to be performed independently on each element of a tensor, enabling parallel execution of learning operations, and accordingly, GPU-accelerated sampling with significant runtime improvements compared to state-of-the-art heuristic samplers. We demonstrate the superior runtime performance of {\sc Demotic} in the sampling task across various CircuitSAT instances from the ISCAS-85 benchmark suite. Specifically, {\sc Demotic} outperforms the state-of-the-art sampler by more than two orders of magnitude in most cases.