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DTSTART;TZID=America/Los_Angeles:20250623T180000
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UID:dac_DAC 2025_sess287_LBR120@linklings.com
SUMMARY:Late Breaking Results: Novel Design of MTJ-Based Unified LIF Spiki
 ng Neuron and PUF
DESCRIPTION:Milad Tanavardi Nasab, Wu Yang, and Himanshu Thapliyal (Univer
 sity of Tennessee, Knoxville)\n\nDue to the higher energy and hardware eff
 iciency 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 secu
 rity applications like device authentication, embedded licensing, device-s
 pecific cryptographic key generation, and anti-counterfeiting. Therefore, 
 PUFs can be used to enhance the security of SNN. Accordingly, in this pape
 r, 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 simu
 lation 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 prop
 osed design is 0.9974, which is close to its ideal value of 1.\n\n
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