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A scalable neural network emulator with MRAM-based mixed-signal circuits.
Jua Lee1,2, Jiho Song1, Hyeon Seong Im1
1College of Information and Communication Engineering, Sungkyunkwan University (SKKU), Suwon, Republic of Korea.
Frontiers in Neuroscience
|June 24, 2025
Summary
This study introduces a novel framework using Magneto-resistive Random Access Memory (MRAM) to mimic brain functions on silicon. The system accurately emulates neural dynamics, paving the way for advanced neuromorphic computing.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Computational Neuroscience
Background:
- Biological neural networks inspire advanced computing architectures.
- Understanding complex neural systems is a significant scientific challenge.
- Magneto-resistive Random Access Memory (MRAM) offers potential for non-volatile, CMOS-compatible neuromorphic devices.
Purpose of the Study:
- To develop a mixed-signal framework for emulating biological neural network behaviors using MRAM.
- To address the limitations of MRAM's low on/off resistance ratio in analog computation.
- To demonstrate robust analog neural processing with MRAM for scalable neural emulation.
Main Methods:
- Integration of multi-bit MRAM synapse arrays with analog circuits.
- Implementation of a current subtraction architecture to generate multi-level synaptic currents.
- Design of a chip with adjustable operating frequency for flexible time-scale emulation.
Main Results:
- Successful emulation of key neural functions: Leaky Integrate and Fire (LIF) dynamics, Excitatory and Inhibitory Postsynaptic Potentials (EPSP/IPSP), refractory period, and lateral inhibition.
- Demonstration of robust analog neural processing overcoming MRAM's low on/off ratio limitations.
- Experimental validation of biologically inspired neural dynamics on fabricated chips.
Conclusions:
- The proposed MRAM-based framework enables accurate emulation of biological neural dynamics.
- This approach facilitates scalable and real-time analog neuromorphic computation.
- The developed technology holds promise for advancing brain-inspired computing systems.
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