Efficient Forward-Only Training for Brain-Inspired Hyperdimensional Computing

Hyperdimensional (HD) computing is an emerging paradigm inspired by human cognition, utilizing high-dimensional vectors to represent and learn information in a lightweight manner based on its simple and efficient operations. In HD-based learning frameworks, the encoding of the high dimensional representations is the most contributing procedure to accuracy and efficiency. However, throughout HD computing's history, the encoder has largely remained static, which leads to sub-optimal hypervector representations and excessive dimensionality requirements. In this paper, we propose novel forward-only training methods for HD encoders, Stochastic Error Projection (SEP) and Input Modulated Projection (IMP), which dynamically adjust the encoding process during training. Our methods achieve accuracies comparable to state-of-the-art HD-based techniques, with SEP and IMP outperforming existing methods by 5.49% on average at a reduced dimensionality of D = 3,000. This reduction in dimensionality results in a 3.32x faster inference.