An integrated toolbox for creating neuromorphic edge applications
spiking neural networks (SNNs) and neuromorphic models are believed to be more efficient in general and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small...
Saved in:
Main Authors: | Lars Niedermeier, Nikil Dutt, Jeffrey L Krichmar |
---|---|
Format: | Article |
Language: | English |
Published: |
IOP Publishing
2025-01-01
|
Series: | Neuromorphic Computing and Engineering |
Subjects: | |
Online Access: | https://doi.org/10.1088/2634-4386/adad0f |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Scalable network emulation on analog neuromorphic hardware
by: Elias Arnold, et al.
Published: (2025-02-01) -
Multilayer magnetic skyrmion devices for spiking neural networks
by: Aijaz H Lone, et al.
Published: (2025-01-01) -
Charge-Based Compact Modeling of OECTs for Neuromorphic Applications
by: Ghader Darbandy, et al.
Published: (2025-01-01) -
Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature
by: Catarina Brites, et al.
Published: (2025-01-01) -
Emerging Spatiotemporal Dynamics in Multiterminal Neuromorphic Nanowire Networks Through Conductance Matrices and Voltage Maps
by: Davide Pilati, et al.
Published: (2024-12-01)