Late Breaking Results: Novel Design of MTJ-Based Unified LIF Spiking Neuron and PUF - Nasab, Milad Tanavardi; Yang, Wu; Thapliyal, Himanshu (2025 62nd ACM/IEEE Design Automation Conference (DAC), 2025)
Due to the higher energy and hardware efficiency of spiking neural networks (SNNs) compared to deep neural networks, they have attracted a lot of attention. However, their security must be investigated, given that they have access to private and confidential data. Physically unclonable functions (PUFs) are a class of circuits with security applications like device authentication, embedded licensing, device-specific cryptographic key generation, and anti-counterfeiting. Therefore, PUFs can be used to enhance the security of SNN. Accordingly, in this paper, an MTJ-based LIF Neuron/PUF has been proposed. The proposed design can function as both a LIF neuron and PUF. The results of the Monte Carlo simulation show that the proposed design has better uniqueness and uniformity values compared to its counterparts. These values for the proposed design are 50.07 % and 49.66 %, which are close to their ideal value of 50 %. Also, the mean value of Shannon entropy for the 128-bit PUF response of the proposed design is 0.9974, which is close to its ideal value of 1.