Lightweight Concurrent Out-of-Distribution Detection in Hyperdimensional Computing Hardware
Mahboobe Sadeghipour Roodsari, Vincent Meyers, Mehdi Baradaran Tahoori · 2025
HyperDimensional Computing (HDC) is a brain-inspired machine learning (ML) approach for cognitive tasks, where input data is transformed and encoded as high dimensional hypervectors and are then compared to aggregated class hypervec-tors for classification. Due to its computationally lightweight operations and noise resilience, it is well suited for resource-constrained edge Artificial Intelligence (AI). A well-known problem in ML tasks is dealing with inputs that are significantly different from the training and test data, which is referred to as Out-of-Distribution (OOD) inputs. When AI models are faced with such inputs, they behave incorrectly which can lead to safety violations, when they are deployed in safety-critical applications. Therefore, detecting OOD inputs is essential for maintaining the functional safety of machine learning accelerators in practice. In this work, we propose an extremely lightweight concurrent OOD detection mechanism in HDC hardware accelerators. Our results not only demonstrate higher OOD detection compared to other state of the arts but also requires no retraining, minimal hardware overhead (2 LUT, 1 Register), and does not introduce additional latency.