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Neural Architecture Search (NAS) is an automated architecture engineeringmethod for deep learning design automation, which serves as an alternative tothe manual and error-prone process of model development, selection, evaluationand performance estimation. However, one major obstacle of NAS is the extremelydemanding computation resource requirements and time-consuming iterationsparticularly when the dataset scales. In this paper, targeting at the emergingvision transformer (ViT), we present NasHD, a hyperdimensional computing basedsupervised learning model to rank the performance given the architectures andconfigurations. Different from other learning based methods, NasHD is fasterthanks to the high parallel processing of HDC architecture. We also evaluatedtwo HDC encoding schemes: Gram-based and Record-based of NasHD on theirperformance and efficiency. On the VIMER-UFO benchmark dataset of 8applications from a diverse range of domains, NasHD Record can rank theperformance of nearly 100K vision transformer models with about 1 minute whilestill achieving comparable results with sophisticated models.

NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing