Document Type
Conference Proceeding
Publication Date
9-9-2024
Manuscript Version
am
Abstract
Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm introduced to achieve energy efficiency with a lightweight and single-pass training model. Hypervectors (HVs) at the heart of the HDC systems play a fundamental role in elevating the accuracy and obtaining the desired performance. Image-based HV encoding requires two types of HVs: Position and Level HVs. State-of-the-art approaches utilize pseudo-random methods for generating these HVs, which might degrade system performance and cause higher power consumption due to poor randomness in HV generation. These conventional methods require iteratively calculating orthogonal Positional HVs for acceptable accuracy. This work proposes a fast, ultra-lightweight, and high-quality HV generator incorporating low-discrepancy random sequences and the emerging unary bit-stream processing. For the first time, we employ unary computing (UC) to generate Level HVs, demonstrating that there is no need for randomness in HDC systems. We generate Position HVs using a single-source quasi-random sequence with a recurrence property. Our proposed HV generation technique improves the overall HDC accuracy by up to 6.4% for the medical MNIST dataset while reducing the power consumption of HV generation by 98%.
Keywords
hyperdimensional computing, low-discrepancy sequences, lowpower AI, random number generators, unary computing
Language
English
Publication Title
The 29th ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED)
Rights
This is a peer reviewed Accepted Manuscript version of this article and is available through CWRU's Faculty Open Access Policy
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Mehran Shoushtari Moghadam, Sercan Aygun, Faeze S. Banitaba, and M. Hassan Najafi. All You Need is Unary: End-to-End Unary Bit-stream Processing in Hyperdimensional Computing. In Proceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED '24). Association for Computing Machinery, New York, NY, USA, 1–6. https://doi.org/10.1145/3665314.3670834
Included in
Computer and Systems Architecture Commons, Digital Circuits Commons, Hardware Systems Commons