.png){kind=icon}
Return
Sparse neuromorphic computing based on spin-torque diodes
.png){kind=icon}
PRE
AI
DOI：10.1063/1.5090566.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We report on the sparse neuromorphic computing based on spin-torque diodes (STDs). The rectification characteristics of STDs have been investigated in the absence and presence of d.c. bias currents. While the injection locking phenomenon is observed in our devices, the output functions versus the d.c. bias currents mimic artificial neurons with sparse representations. Furthermore, we construct a neural network with STD neurons to recognize the handwritten digits in the Mixed National Institute of Standards and Technology database, with a produced accuracy of up to 92.7%. The results suggest that STDs have potential to be building blocks for the realization of a biologically plausible neuromorphic computing system.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
10.4W
Citations:
17.8W
Organization
View more organizations
Cited Papers
Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing.png)
NANO LETTERS
NANO LETTERSIF9.1
PREAI
ShareSaveShareSaveShareSaveShareSaveStudying antimicrobial-induced morphostructural damage of bacteria by Scanning Electron Microscope
OAAI
OAAIShareSave View more