Configurable p-Neurons Using Modular p-Bits
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917224780922880 |
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| author | Bunaiyan, Saleh Alsharif, Mohammad Abdelrahman, Abdelrahman S. ElSawy, Hesham Cheema, Suraj S. Fahmy, Suhaib A. Camsari, Kerem Y. Al-Dirini, Feras |
| author_facet | Bunaiyan, Saleh Alsharif, Mohammad Abdelrahman, Abdelrahman S. ElSawy, Hesham Cheema, Suraj S. Fahmy, Suhaib A. Camsari, Kerem Y. Al-Dirini, Feras |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18943 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Configurable p-Neurons Using Modular p-Bits Bunaiyan, Saleh Alsharif, Mohammad Abdelrahman, Abdelrahman S. ElSawy, Hesham Cheema, Suraj S. Fahmy, Suhaib A. Camsari, Kerem Y. Al-Dirini, Feras Emerging Technologies Disordered Systems and Neural Networks Artificial Intelligence Hardware Architecture Machine Learning 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. |
| title | Configurable p-Neurons Using Modular p-Bits |
| topic | Emerging Technologies Disordered Systems and Neural Networks Artificial Intelligence Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2601.18943 |