Abstract
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.
Original language | English (US) |
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Pages (from-to) | 3464-3468 |
Number of pages | 5 |
Journal | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
Volume | 42 |
Issue number | 10 |
DOIs | |
State | Published - Oct 1 2023 |
All Science Journal Classification (ASJC) codes
- Software
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering