LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis

The majority of existing frameworks for automated synthesis of Approximate Circuits (AxCs) employ a search-based Design Space Exploration (DSE) approach. This includes an iterative process where approximate circuit instances are created and evaluated in terms of quality and performance metrics. The quality evaluation of each instance via either verification or testing results in extremely long computation times, imposing a practical limit on the number of nodes that can be explored in the design space. To overcome this problem, we exploit Random Forests (RFs) to develop extremely fast estimators to evaluate the quality and performance of AxC instances while avoiding time-consuming computations. Utilizing these estimators we build LDAX, an efficient design space exploration framework that attempts to improve the runtime of the AxCs synthesis process. LDAX is based on the fact that, in an iterative search space exploration, a large number of validations can be skipped by leveraging a high-accuracy predictor. To efficiently explore the design space, which is usually represented by a tree and each node denotes an AxC, we propose a learning-based search technique that can quickly analyze a large set of nodes even very deep nodes in the tree. Our experimental results reveal that LDAX can achieve average speed-up of 41 × on a set of practical benchmarks from different application domains in comparison with the most competitive state-of-the-art framework.