dorsal/arxiv
View SchemaProbabilistic Computers for MIMO Detection: From Sparsification to 2D Parallel Tempering
| Authors | M Mahmudul Hasan Sajeeb, Corentin Delacour, Kevin Callahan-Coray, Sanjay Seshan, Tathagata Srimani, Kerem Y. Camsari |
|---|---|
| Categories | |
| ArXiv ID | 2601.09037vv1 |
| URL | https://arxiv.org/abs/2601.09037 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Probabilistic computers built from p-bits offer a promising path for combinatorial optimization, but the dense connectivity required by real-world problems scales poorly in hardware. Here, we address this through graph sparsification with auxiliary copy variables and demonstrate a fully on-chip parallel tempering solver on an FPGA. Targeting MIMO detection, a dense, NP-hard problem central to wireless communications, we fit 15 temperature replicas of a 128-node sparsified system (1,920 p-bits) entirely on-chip and achieve bit error rates significantly below conventional linear detectors. We report complete end-to-end solution times of 4.7 ms per instance, with all loading, sampling, readout, and verification overheads included. ASIC projections in 7 nm technology indicate about 90 MHz operation with less than 200 mW power dissipation, suggesting that massive parallelism across multiple chips could approach the throughput demands of next-generation wireless systems. However, sparsification introduces sensitivity to the copy-constraint strength. Employing Two-Dimensional Parallel Tempering (2D-PT), which exchanges replicas across both temperature and constraint dimensions, we demonstrate over 10X faster convergence without manual parameter tuning. These results establish an on-chip p-bit architecture and a scalable algorithmic framework for dense combinatorial optimization.
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"abstract": "Probabilistic computers built from p-bits offer a promising path for combinatorial optimization, but the dense connectivity required by real-world problems scales poorly in hardware. Here, we address this through graph sparsification with auxiliary copy variables and demonstrate a fully on-chip parallel tempering solver on an FPGA. Targeting MIMO detection, a dense, NP-hard problem central to wireless communications, we fit 15 temperature replicas of a 128-node sparsified system (1,920 p-bits) entirely on-chip and achieve bit error rates significantly below conventional linear detectors. We report complete end-to-end solution times of 4.7 ms per instance, with all loading, sampling, readout, and verification overheads included. ASIC projections in 7 nm technology indicate about 90 MHz operation with less than 200 mW power dissipation, suggesting that massive parallelism across multiple chips could approach the throughput demands of next-generation wireless systems. However, sparsification introduces sensitivity to the copy-constraint strength. Employing Two-Dimensional Parallel Tempering (2D-PT), which exchanges replicas across both temperature and constraint dimensions, we demonstrate over 10X faster convergence without manual parameter tuning. These results establish an on-chip p-bit architecture and a scalable algorithmic framework for dense combinatorial optimization.",
"arxiv_id": "2601.09037",
"authors": [
"M Mahmudul Hasan Sajeeb",
"Corentin Delacour",
"Kevin Callahan-Coray",
"Sanjay Seshan",
"Tathagata Srimani",
"Kerem Y. Camsari"
],
"categories": [
"cs.ET",
"cond-mat.dis-nn",
"cs.DC"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Probabilistic Computers for MIMO Detection: From Sparsification to 2D Parallel Tempering",
"url": "https://arxiv.org/abs/2601.09037",
"version": "v1"
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