| Autor: |
Yang, Kezhou, G M, Dhuruva Priyan, Sengupta, Abhronil |
| Zdroj: |
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems; October 2023, Vol. 42 Issue: 10 p3464-3468, 5p |
| Abstrakt: |
Brain-inspired computing—leveraging neuroscientific principles underpinning the unparalleled efficiency of the brain in solving cognitive tasks—is emerging to be a promising pathway to solve several algorithmic and computational challenges faced by deep learning today. Nonetheless, current research in neuromorphic computing is driven by our well-developed notions of running deep learning algorithms on computing platforms that perform deterministic operations. In this article, we argue that taking a different route of performing temporal information encoding in probabilistic neuromorphic systems may help solve some of the current challenges in the field. The article considers superparamagnetic tunnel junctions as a potential pathway to enable a new generation of brain-inspired computing that combines the facets and associated advantages of two complementary insights from computational neuroscience: 1) how information is encoded and 2) how computing occurs in the brain. The hardware-algorithm co-design analysis demonstrates 97.41% accuracy of a state-compressed 3-layer spintronics-enabled stochastic spiking network on the MNIST dataset with high spiking sparsity due to temporal information encoding. |
| Databáze: |
Supplemental Index |
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