MCAC: A Model Counting Algorithm for Exact Computation of Error Metrics of Approximate Circuits

TL;DR

MCAC introduces a unified model counting framework that efficiently computes multiple error metrics for approximate circuits using a single error miter and message passing, outperforming existing methods.

Contribution

The paper presents a novel, unified approach for exact error metric computation in approximate circuits, reducing computational effort compared to traditional methods.

Findings

Significant speedup over existing model counters.

Single miter approach for multiple error metrics.

Efficient data structures for sparse computations.

Abstract

Effective usage of approximate circuits for various performance trade-offs requires accurate computation of error. MCAC is a novel model counting framework for exact computation of several average and worst-case error metrics that are used to evaluate approximate circuits. Unlike other methods in the literature, our framework uses the same error miter for all metrics. It requires a single synthesis of the system consisting of the exact and approximate circuits followed by a subtractor that finds the difference of the two outputs. Existing miter-based methods require multiple calls to the model counter, one for each output of the miter. MCAC uses the CNF formula of the system to compute all metrics. Our algorithm converts the formula to a tree and uses message passing to compute all metrics. We propose data structures to efficiently store and perform sparse computations required for conversion to a tree and message passing. Results for all the error metrics for several benchmark instances show a significant speedup over using off-the-shelf model counters along with specialized miters for each metric.