发表机构
Princeton University; Cornell University; Fermilab（普林斯顿大学; 康奈尔大学; 费米国家加速器实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估逻辑神经网络在粒子物理像素探测器中的片上推理，利用其资源效率实现复杂架构，增强抗辐射鲁棒性，并提出可重构LNN以应对部署后条件变化。
AI 中文摘要
在极端数据速率或资源限制使得集中处理不可行的应用中，设备端机器学习正变得越来越重要。逻辑神经网络（LNNs）直接学习布尔逻辑而非传统算术运算，最近已成为一种高效的推理方法，特别适合部署在定制硅片上。然而，硬连线解决方案通常针对狭义任务进行优化，缺乏适应不断变化的数据分布的能力。粒子物理中的下一代像素探测器呈现出一个特别具有挑战性的用例：其高粒度和读出频率产生的数据速率无法集中处理，而严格的空间和功率限制要求高度高效的片上推理。此外，辐射损伤会逐渐改变探测器响应，需要可在部署后重新训练和重新配置的模型。在这项工作中，我们评估了LNNs在像素探测器中的片上推理性能。我们表明，其卓越的资源效率可能带来模型能力的阶跃变化，使得在可用硬件预算内实现更复杂的架构成为可能。LNNs还对辐射引起的随机比特翻转提供了更高的鲁棒性。最后，我们提出了关于可重构LNNs的新工作，使模型能够在部署后适应不断变化的操作条件。
英文摘要
On-device machine learning is increasingly important in applications where extreme data rates or resource constraints make centralized processing infeasible. Logic neural networks (LNNs), which directly learn Boolean logic instead of conventional arithmetic operations, have recently emerged as a state-of-the-art approach for efficient inference and are particularly well suited for deployment in custom silicon. However, hard-wired solutions are typically optimized for narrowly defined tasks and lack the ability to adapt to changing data distributions. Next-generation pixel detectors in particle physics present a particularly challenging use case: their high granularity and readout frequency generate data rates that cannot be centrally processed, while stringent space and power constraints necessitate highly efficient on-chip inference. Moreover, radiation damage progressively alters detector response, requiring models that can be re-trained and reconfigured after deployment. In this work, we evaluate LNNs for on-chip inference in pixel detectors. We show that their superior resource efficiency may enable a step change in model capability, making more complex architectures feasible within the available hardware budget. LNNs also provide increased robustness against radiation-induced random bit flips. Finally, we present novel work on reconfigurable LNNs, enabling models to adapt to changing operating conditions after deployment.
Comments8 pages, 3 figures. Accepted as a poster at the NeurIPS 2026 Workshop on On-Device Intelligence: Foundation Models under Real-World Constraints (ODI 2026)
