Emergent Synaptic Plasticity from Tunable Dynamics of Probabilistic Bits
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910924296683520 |
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| author | Banerjee, Sagnik Konakanchi, Shiva T. Datta, Supriyo Upadhyaya, Pramey |
| author_facet | Banerjee, Sagnik Konakanchi, Shiva T. Datta, Supriyo Upadhyaya, Pramey |
| contents | Probabilistic (p-) computing, which leverages the stochasticity of its building blocks (p-bits) to solve a variety of computationally hard problems, has recently emerged as a promising physics-inspired hardware accelerator platform. A functionality of importance for p-computers is the ability to program-and reprogram-the interaction strength between arbitrary p-bits on-chip. In natural systems subject to random fluctuations, it is known that spatiotemporal noise can interact with the system's nonlinearities to render useful functionalities. Leveraging that principle, here we introduce a novel scheme for tunable coupling that inserts a ''hidden'' p-bit between each pair of computational p-bits. By modulating the fluctuation rate of the hidden p-bit relative to the synapse speed, we demonstrate both numerically and analytically that the effective interaction between the computational p-bits can be continuously tuned. Moreover, this tunability is directional, where the effective coupling from one computational p-bit to another can be made different from the reverse. This synaptic-plasticity mechanism could open new avenues for designing (re-)configurable p-computers and may inspire novel algorithms that leverage dynamic, hardware-level tuning of stochastic interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00252 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emergent Synaptic Plasticity from Tunable Dynamics of Probabilistic Bits Banerjee, Sagnik Konakanchi, Shiva T. Datta, Supriyo Upadhyaya, Pramey Disordered Systems and Neural Networks Applied Physics Computational Physics Probabilistic (p-) computing, which leverages the stochasticity of its building blocks (p-bits) to solve a variety of computationally hard problems, has recently emerged as a promising physics-inspired hardware accelerator platform. A functionality of importance for p-computers is the ability to program-and reprogram-the interaction strength between arbitrary p-bits on-chip. In natural systems subject to random fluctuations, it is known that spatiotemporal noise can interact with the system's nonlinearities to render useful functionalities. Leveraging that principle, here we introduce a novel scheme for tunable coupling that inserts a ''hidden'' p-bit between each pair of computational p-bits. By modulating the fluctuation rate of the hidden p-bit relative to the synapse speed, we demonstrate both numerically and analytically that the effective interaction between the computational p-bits can be continuously tuned. Moreover, this tunability is directional, where the effective coupling from one computational p-bit to another can be made different from the reverse. This synaptic-plasticity mechanism could open new avenues for designing (re-)configurable p-computers and may inspire novel algorithms that leverage dynamic, hardware-level tuning of stochastic interactions. |
| title | Emergent Synaptic Plasticity from Tunable Dynamics of Probabilistic Bits |
| topic | Disordered Systems and Neural Networks Applied Physics Computational Physics |
| url | https://arxiv.org/abs/2505.00252 |