Towards Forward-Only Learning for Hyperdimensional Computing

Hyperdimensional (HD) Computing is a lightweight representation system that symbolizes data as high-dimensioned vectors. HD computing has been growing in popularity in recent years as an alternative to deep neural networks mainly due to its simple and efficient operations. In HD-based learning frameworks, the encoding of the high dimensional representations are widely cited to be the most contributing procedure to accuracy and efficiency. However, throughout HD computing's history, the encoder has largely remained static. In this work, we explore methods for a dynamic encoder that yields better representations as training progresses. Our proposed method, SEP, achieves accuracies comparable to state-of-the-art HD-based methods proposed in the literature; more notably, our solutions outperform existing work at lower dimensions while maintaining a relatively small dimension of D=3,000, which equates to an average of 3.32× faster inference.