We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.
翻译:我们运用基于深卷动区块的物体探测技术,完成核核核核反应堆大型散射相撞器(LHC)中遇到的端到端的喷射识别和重建任务。在LHC产生的碰撞事件,作为由热度计和跟踪细胞组成的图像,作为单一镜头探测网络的一种输入。名为PFJet-SSD的算法,同时对喷射机进行定位、分类和回归任务,并重建其特征。这一全在单向向前传送的传球在执行时间和改进精确度的传统基于规则的方法方面具有优势。进一步收益来自网络的微缩、同质量化和优化运行时间,以便满足典型实时处理环境的记忆和耐久性限制。我们试验了8位和定点的精确度和推导力,以单向偏移浮点为基准。我们显示,红外网络在完全精度等值和超出州际应用系统时的性能。最后,我们在基于不同的硬度定位平台上对硬度报告进行了试验。