Configurable p-Neurons Using Modular p-Bits

• URL: http://arxiv.org/abs/2601.18943v1
• Date: Mon, 26 Jan 2026 20:32:54 GMT
• Title: Configurable p-Neurons Using Modular p-Bits
• Authors: Saleh Bunaiyan, Mohammad Alsharif, Abdelrahman S. Abdelrahman, Hesham ElSawy, Suraj S. Cheema, Suhaib A. Fahmy, Kerem Y. Camsari, Feras Al-Dirini,
• Abstract summary: We re-engineer the p-bit by decoupling its signal path from its input data path, giving rise to a modular p-bit.<n>We present spintronic (CMOS + sMTJ) designs that show wide ranges of operation.<n>We implement digital-CMOS versions on an FPGA, with tunable unit sharing, and demonstrate an order of magnitude (10x) saving in required hardware resources.
• Score: 3.7531225251442706
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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