Assertion-aware approximate computing design exploration on behavioral models

Approximate Computing (AxC) aims at optimizing the hardware resources in terms of area and power consumption at the cost of a reasonable degradation in computation accuracy. Several design exploration approaches and metrics have been proposed so far to identify the approximation targets, but only a few of them exploit information derived from assertion-based verification (ABV). To fill in the gap, in this paper, we propose an ABV-based methodology to guide the AxC design exploration of behavioral descriptions. Assertions are automatically mined from the simulation traces of the original design to capture the golden behaviours. Then, we define a metric to predict the impact of approximating model statements on the design accuracy. The metric is computed by a function based on the syntax tree of the mined assertions, their support, and the information derived from the variable dependency graph of the design. It is therefore used, together with functional coverage information, in a sorting procedure for AxC design space exploration to select the target statements for approximation.