A scalable neural network emulator with MRAM-based mixed-signal circuits
In this study, we present a mixed-signal framework that utilizes MRAM (Magneto-resistive Random Access Memory) technology to emulate behaviors observed in biological neural networks on silicon substrates. While modern technology increasingly draws inspiration from biological neural networks, fully u...
Saved in:
| Main Authors: | Jua Lee, Jiho Song, Hyeon Seong Im, Jonghwi Kim, Woonjae Lee, Wooseok Yi, Soonwan Kwon, Byungsu Jung, Joohyoung Kim, Yoonmyung Lee, Jung-Hoon Chun |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1599144/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing On-Device DNN Inference Performance With a Reduced Retention-Time MRAM-Based Memory Architecture
by: Munhyung Lee, et al.
Published: (2024-01-01) -
Specific Neural Coding of Complex Neural Network Based on Time Coding Under Various Exterior Stimuli
by: Lei Guo, et al.
Published: (2025-03-01) -
Enhancing Task-Incremental Learning via a Prompt-Based Hybrid Convolutional Neural Networks (CNNs)-Vision Transformer (ViT) Framework
by: Zuomin Yang, et al.
Published: (2025-01-01) -
Enhancing temporal learning in recurrent spiking networks for neuromorphic applications
by: Ismael Balafrej, et al.
Published: (2025-01-01) -
Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation
by: Kumiko Nomura, et al.
Published: (2024-11-01)