芯片级概率推断用于传感器边缘的带电粒子跟踪
On-chip probabilistic inference for charged-particle tracking at the sensor edge
AI总结：
研究提出在传感器边缘利用嵌入式神经网络进行带电粒子跟踪，实现高精度、低延迟的参数推断，为高率科学仪器的智能传感提供新途径。
摘要：
现代科学仪器面临日益严峻的带宽、延迟和功率约束。传感器边缘的推断决定了数据收集效率，通过决定保存哪些信息供进一步分析。大型强子对撞机的粒子跟踪探测器展示了这一挑战：像素化的硅传感器产生丰富的时空电离模式，但大部分信息因数据速率限制被丢弃。同时，联合设计工具的进步使将机器学习整合到应用特定集成电路中变得更加迅速，推动了新型集成技术的粒子探测器设计。我们证明嵌入前端电子中的神经网络可以从单个硅层推断带电粒子的动力学参数。我们回归击中位置和入射角度并校准不确定性，同时满足对数值精度、延迟和硅面积的严格约束。我们的结果为在边缘进行概率推断铺平了道路，为高率科学仪器的智能传感开辟了新机会。
英文摘要：
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.
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