HDCircuit: Brain-Inspired HyperDimensional Computing for Circuit Recognition

Circuits possess a non-Euclidean representation, necessitating the encoding of their data structure (e.g., gate-level netlists) into fixed formats like vectors. This work is the first to propose brain-inspired hyperdimensional computing (HDC) for optimized circuit encoding. HDC does not require extensive training to encode a gate-level netlist into a hypervector and simplifies the similarity check between circuits from graph-based to the similarity between their hypervectors. We introduce a versatile HDC-based encoding method for circuit encoding. We demonstrate its effectiveness with the application of circuit recognition using ITC-99 and ISCAS-85 benchmarks. We maintain a 98.2% accuracy, even when the designs are obfuscated using logic locking.