FastLEC: Parallel Datapath Equivalence Checking with Hybrid Engines

Abstract Combinational equivalence checking (CEC) remains a challenge EDA task in the formal verification of datapath circuits due to their complex arithmetic structures and the limited capability or scalability of SAT, BDD, and exact-simulation (ES) based techniques when used independently. This work presents FastLEC , a hybrid prover that unifies these three formal reasoning engines and introduces three strategies that substantially enhance verification efficiency. First, a regression-based engine-scheduling heuristic predicts solver effectiveness, enabling more accurate and balanced allocation of computational resources. Second, datapath-structure-aware partitioning strategies, along with a dynamic divide-and-conquer SAT prover, exploit the regularity of arithmetic designs while preserving completeness. Third, the memory overhead of ES is significantly reduced through address-reference-count tracking, and simulation is further accelerated through a GPU-enabled backend. FastLEC is evaluated across 368 datapath circuits. Using 32 CPU cores, it proves 5.07 $$\times $$ × more circuits than the widely used ABC &cec tool. Compared with the latest best datapath-oriented serial and parallel CEC provers, FastLEC outperforms them by 3.33 $$\times $$ × and 2.67 $$\times $$ × in PAR-2 time, demonstrating an improvement of 74 newly solved circuits. With the addition of a single GPU, it achieves a further 4.07 $$\times $$ × improvement. The prover also demonstrates excellent scalability.