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Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework by Zou, Zhuowen; Alimohamadi, Haleh; Imani, Farhad; Kim, Yeseong; Imani, Mohsen is a scholarly article available to read on EtoBox.

What is Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework about?

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,

Author

Zou, Zhuowen; Alimohamadi, Haleh; Imani, Farhad; Kim, Yeseong; Imani, Mohsen

Published

2021

Language

EN