Configurable p-Neurons Using Modular p-Bits

Saleh Bunaiyan; Mohammad Alsharif; Abdelrahman S. Abdelrahman; Hesham ElSawy; Suraj S. Cheema; Suhaib A. Fahmy; Kerem Y. Camsari; Feras Al-Dirini

arXiv · 2601.18943

Configurable p-Neurons Using Modular p-Bits

Saleh Bunaiyan·Mohammad Alsharif·Abdelrahman S. Abdelrahman·Hesham ElSawy·Suraj S. Cheema·Suhaib A. Fahmy·Kerem Y. Camsari·Feras Al-Dirini

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Abstract

Probabilistic bits (p-bits) have recently been employed in neural networks (NNs) as stochastic neurons with sigmoidal probabilistic activation functions. Nonetheless, there remain a wealth of other probabilistic activation functions that are yet to be explored. Here we re-engineer the p-bit by decoupling its stochastic signal path from its input data path, giving rise to a modular p-bit that enables the realization of probabilistic neurons (p-neurons) with a range of configurable probabilistic activation functions, including a probabilistic version of the widely used Logistic Sigmoid, Tanh and Rectified Linear Unit (ReLU) activation functions. We present spintronic (CMOS + sMTJ) designs that show wide and tunable probabilistic ranges of operation. Finally, we experimentally implement digital-CMOS versions on an FPGA, with stochastic unit sharing, and demonstrate an order of magnitude (10x) saving in required hardware resources compared to conventional digital p-bit implementations.

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Saleh Bunaiyan, Mohammad Alsharif, Abdelrahman S. Abdelrahman, Hesham ElSawy, Suraj S. Cheema, Suhaib A. Fahmy, Kerem Y. Camsari, Feras Al-Dirini. 2026-01-26. Configurable p-Neurons Using Modular p-Bits. https://arxiv.org/abs/2601.18943

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