Machine Learning Algorithms: From GPU to FPGA to ASIC

该项目建立在学生、Rinnert和FBK(Marco Christoferetti)的成功演示(CHEP，2018年9月)的基础上，即机器学习可以用于粒子物理中的轨迹重建。这是世界上第一次。在这里，我们希望学生证明人工智能(映射到FPGA上，然后是一个定制芯片)是(部分)解决HEP计算的国际赤字问题的潜在候选者。该博士学位的主要主题将是将该算法的简化版本部署在一个FPGA上，以演示CPU的“挂钟”性能(一个关键参数)。这种混合算法将与学生也将开发的强化学习算法的速度和性能进行比较。Mapable算法的开发和选择大约需要6个月的工作。现场可编程门阵列的版本和编程还需要一年的时间。剩下的时间将与FBK--也是这项技术的利益相关者--合作，生产ASIC和支撑板，以证明可以将FPGA模型转移到低成本ASIC。该项目的设计和成本将由PP承保。对英国社区来说，一个有效的解决方案可能价值高达1亿GB，并适用于基础物理以外的领域。例如，用“可编程的”寄存器ASIC取代昂贵的跟踪系统，有望降低质子计算机断层扫描系统的成本。学生将在PP和后来的FBK之间工作。PP有2名世界级的FPGA程序员可以参与其中。所有这方面的费用将由PP承担，我们有信心风险很低。PP和FBK将帮助评估FPGA模型的性能，并将其映射到ASIC上的设计。这是对现有CDT方案的新的和重要的发展，以及我们与FBK的战略联系，以提供潜在的世界一流的创新成果，并在CERN和FNAL提供一个展示成果的世界舞台。

The project builds on the successful demonstration (CHEP, Sept 2018) by student, Rinnert and FBK (Marco Christoferetti) that Machine Learning can be used for track reconstruction in particle physics. This was a world first. Here we want the student to demonstrate that AI (mapped onto FPGA then a custom chip) is a potential candidate for (part) solving the problem of the international deficit of computing for HEP. The principle theme of this PhD will be to take a simplified version of this algorithm and deploy it on an FPGA to demonstrate the "wall clock" performance (a key parameter) v CPU. This hybrid algorithm will be compared against the speed and performance of reinforced learning algorithms which the student will also develop. The development and selection of the mapable algorithm is approximately 6 months of work. The FPGA version and its programming will take a further year. The remaining time will be working with FBK - also a stakeholder in this technology - to produce the ASIC and support boards required to demonstrate that the FPGA model can be transferred to a low cost ASIC. Design and costs of this project will be underwritten by PP. A working solution could be worth up to £100m to the UK community, and has applications outside of fundamental physics. For example, replacing expensive tracking systems with "programmable" register ASICs offers the promise to decrease costs for proton computed tomography systems.The student will work between PP and latterly FBK . PP has 2 world class FPGA programmers who can participate in this. All cost for this will be borne by PP and we are confident of low risk. PP and FBK will help evaluate the performance of the FPGA model and map this to a design on an ASIC. This is novel and important evolution of the existing CDT programme and our strategic links with FBK to deliver a potentially world beating and innovative results with a world stage at CERN and FNAL to present results.