A Scalable Neural Network Emulator with MRAM-Based Mixed-Signal Circuits

Jua Lee^1,2Jiho Song²Hyeon Seong Im²Jonghwi Kim²Woonjae Lee²Wooseok Yi³Soonwan Kwon³Byungsu Jung³Joohyoung Kim⁴Yoonmyung Lee²Jung-Hoon Chun^2*

• ¹Samsung (South Korea), Seoul, Republic of Korea
• ²College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Gyeonggi, Republic of Korea
• ³Samsung Advanced Institute of Technology (SAIT), Gyeonggi-do, Republic of Korea
• ⁴Interuniversity Microelectronics Centre (IMEC), Leuven, Belgium

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 understanding these complex systems remains a significant challenge. Our framework integrates multi-bit MRAM synapse arrays and analog circuits to replicate essential neural functions, including Leaky Integrate and Fire (LIF) dynamics, Excitatory and Inhibitory Postsynaptic Potentials (EPSP and IPSP), the refractory period, and the lateral inhibition. A key challenge in using MRAM for neuromorphic systems is its low on/off resistance ratio, which limits the accuracy of current-mode analog computation. To overcome this, we introduce a current subtraction architecture that reliably generates multi-level synaptic currents based on MRAM states. This enables robust analog neural processing while preserving MRAM's advantages, such as non-volatility and CMOS compatibility. The chip's adjustable operating frequency allows it to replicate biologically realistic time scales as well as accelerate experimental processes. Experimental results from fabricated chips confirm the successful emulation of biologically inspired neural dynamics, demonstrating the feasibility of MRAM-based analog neuromorphic computation for real-time and scalable neural emulation. challenges [2]. These circuits not only emulate the computational capabilities of individual neurons but also employ spiking representations for communication, learning, memory, and computation. However, despite their reliance on biological neural networks as a reference, our understanding of these complex systems remains limited.Research efforts have been actively directed toward capturing more detailed signals in biological neural networks. For instance, recent developments have introduced nano-electrode arrays [7] capable of recording signals in biological neural networks. These arrays allow for the cultivation of neural networks directly on the surface of an integrated circuit, establishing connections with neurons. These developments motivate the need for hardware platforms capable of real-time interaction with biological signals, operating at biologically realistic time scales, and supporting biologically meaningful behaviors such as the refractory period and lateral inhibition.