GPU-Accelerated Synthesis of Mixed-Boolean Arithmetic: Beyond Caching
Synthesizing Mixed-Boolean Arithmetic (MBA) expressions from input-output examples is central to program deobfuscation and also useful for compiler optimization, reverse engineering, and cryptanalysis. Existing MBA synthesizers are typically CPU-based and scale poorly on large specifications or complex targets. Recent GPU-accelerated synthesis methods achieve large speedups in qualitative settings, but they depend on caching observationally equivalent candidates; this strategy breaks down for MBA because candidate outputs are quantitative bitvectors and the behavioral space is enormous. We present SIMBA (Synthesis of Mixed-Boolean Arithmetic), a GPU-accelerated MBA synthesizer built aroun...
| Authors | Gabriel Bathie, Baptiste Mouillon, Nathanaël Fijalkow |
|---|---|
| Primary category | cs.PL |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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