Collide & Conquer: Side-channel Attack on Hyper-dimensional Computing (HDC) Accelerators

Hyper-dimensional computing (HDC), a brain-inspired architecture, is gaining attention for edge AI due to its noise resilience and suitability for resource-constrained environments. However, its deployment in safety-critical domains exposes HDC to critical security vulnerabilities, including data poisoning and intellectual property (IP) theft. We demonstrate a practical side-channel attack on an FPGA-based binary HDC accelerator using voltage fluctuations captured by Time-to-Digital Converters (TDC) sensors to extract its IP, such as class hypervectors. By introducing collision analysis combined with an implicit triggering mechanism, we achieve a maximum of ≈83% bit recovery of a single class hypervector using a few hundred traces, even under parallel operations. We also discuss a randomization counter-measure that effectively reduces the recovery accuracy to ≈19% without sacrificing classification performance.