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Zhongkui Zhang

Publications and source records attributed to Zhongkui Zhang.

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Reconfigurable Physical Unclonable Function based on SOT-MRAM Chips

Hardware-based security primitives have become critical to enhancing information security in the Internet of Things (IoT) era. Physical unclonable functions (PUFs) utilize the inherent variations in the manufacturing process to generate cryptographic keys unique to a device. Reconfigurable PUFs (rPUFs) can update cryptographic keys for enhanced security in dynamic operational scenarios involving huge amounts of data, which makes them suitable for implementation in CMOS-integrated spin-orbit torque magnetic random access memory (SOT-MRAM) chips. However, a key challenge is achieving real-time reconfiguration independent of the environmental conditions, particularly the operating temperature. We propose a dual-pulse reconfiguration strategy for rPUFs in CMOS-integrated SOT-MRAM chips that effectively widens the operating window and achieves resilience across a wide range of operating temperatures without the need for dynamic feedback that overly complicates circuit design. The proposed strategy lays a solid foundation for the next generation of hardware-based security primitives to protect IoT architectures.

cs.CR

QUEST: A Quantized Energy-Aware SNN Training Framework for Multi-State Neuromorphic Devices

Neuromorphic devices, leveraging novel physical phenomena, offer a promising path toward energy-efficient hardware beyond CMOS technology by emulating brain-inspired computation. However, their progress is often limited to proof-of-concept studies due to the lack of flexible spiking neural network (SNN) algorithm frameworks tailored to device-specific characteristics, posing a significant challenge to scalability and practical deployment. To address this, we propose QUEST, a unified co-design framework that directly trains SNN for emerging devices featuring multilevel resistances. With Skyrmionic Magnetic Tunnel Junction (Sk-MTJ) as a case study, experimental results on the CIFAR-10 dataset demonstrate the framework's ability to enable scalable on-device SNN training with minimal energy consumption during both feedforward and backpropagation. By introducing device mapping pattern and activation operation sparsity, QUEST achieves effective trade-offs among high accuracy (89.6%), low bit precision (2-bit), and energy efficiency (93 times improvement over the ANNs). QUEST offers practical design guidelines for both the device and algorithm communities, providing insights to build energy-efficient and large-scale neuromorphic systems.

physics.app-ph