Memory-inspired spiking hyperdimensional network for robust online learning
Abstract
Recently, brain-inspired computing models have shown great potential to outperform today’s deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivate their combination. With the help of the classical psychological model on memory, we propose SpikeHD, the first framework that fundamentally combines Spiking neural network and hyperdimensional computing. SpikeHD generates a scalable and strong cognitive learning system that better mimics brain functionality. SpikeHD exploits spiking neural networks to extract low-level features by preserving the spatial and temporal correlation of raw event-based spike data. Then, it utilizes HDC to operate over SNN output by mapping the signal into high-dimensional space, learning the abstract information, and classifying the data. Our extensive evaluation on a set of benchmark classification problems shows that SpikeHD provides the following benefit compared to SNN architecture: (1) significantly enhance learning capability by exploiting two-stage information processing, (2) enables substantial robustness to noise and failure, and (3) reduces the network size and required parameters to learn complex information.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 10, 2022
- Source ID
- 10.1038/s41598-022-11073-3
Entities
People
- Ali Zakeri
- Farhad Imani
- Haleh Alimohamadi
- M. Hassan Najafi
- Mohsen Imani
- Yeseong Kim
- Zhuowen Zou
Organizations
- Air Force Office of Scientific Research
- Cisco
- Louisiana Board of Regents
- National Science Foundation
- Office of Naval Research
- Office of Naval Research Global
- Semiconductor Research Corporation