Advances in Architectures and Tools for FPGAs and their Impact on the Design of Complex Systems for Particle Physics

The continual improvement of semiconductor technology has provided rapid advancements in device frequency and density. Designers of electronics systems for high-energy physics (HEP) have benefited from these advancements, transitioning many designs from fixed-function ASICs to more flexible FPGA-based platforms. Today’s FPGA devices provide a significantly higher amount of resources than those available during the initial Large Hadron Collider design phase. To take advantage of the capabilities of future FPGAs in the next generation of HEP experiments, designers must not only anticipate further improvements in FPGA hardware, but must also adopt design tools and methodologies that can scale along with that hardware. In this paper, we outline the major trends in FPGA hardware, describe the design challenges these trends will present to developers of HEP electronics, and discuss a range of techniques that can be adopted to overcome these challenges

A Survey of Software Fault Tolerance Techniques

The paper surveys various software fault tolerance techniques and methodologies. The techniques include traditional techniques: recovery blocks (RtB), n-version programming, n selfchecking Programming, retry blocks (RtB), n-copy programming and some new techniques: adaptive n-version systems, fuzzy voting, abstraction, parallel graph reduction, rejuvenation. The utility for each technique based on its attribution has also been presented

Active Magnetic Field Compensation System for SRF Cavities

International audienceAbstract: Superconducting Radio Frequency (SRF) cavities are becoming popular in modern particle accelerators. When the SRF cavity is transitioning from the non-conducting to the Superconducting state at the critical temperature (Tc), the ambient magnetic field can be trapped. This trapped flux may lead to an increase in the surface resistance of the cavity wall, which can reduce the Q-factor and efficiency of the cavity. In order to increase the Q-factor, it is important to lower the surface resistance by reducing the amount of magnetic flux trapped in the cavity wall to sub 10mG range during the Tc transition. In this paper, we present a 3-axis automatic active magnetic field compensation system that is capable of reducing the earth magnetic field and any local disturbance field. Design techniques are described to enhance the system stability while utilizing the flexibility of embedded electronics. This paper describes the system implementation and concludes with initial results of tests. Experimental results demonstrate that the proposed magnetic field compensation system can reduce the earth magnetic field to around 2.5 mG even without shielding

Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications