A Modular Approach to Adaptive Reactive Streaming Systems

The latest generations of FPGA devices offer large resource counts that provide the headroom to implement large-scale and complex systems. However, there are increasing challenges for the designer, not just because of pure size and complexity, but also in harnessing effectively the flexibility and programmability of the FPGA. A central issue is the need to integrate modules from diverse sources to promote modular design and reuse. Further, the capability to perform dynamic partial reconfiguration (DPR) of FPGA devices means that implemented systems can be made reconfigurable, allowing components to be changed during operation. However, use of DPR typically requires low-level planning of the system implementation, adding to the design challenge. This dissertation presents ReShape: a high-level approach for designing systems by interconnecting modules, which gives a ‘plug and play’ look and feel to the designer, is supported by tools that carry out implementation and verification functions, and is carried through to support system reconfiguration during operation. The emphasis is on the inter-module connections and abstracting the communication patterns that are typical between modules – for example, the streaming of data that is common in many FPGA-based systems, or the reading and writing of data to and from memory modules. ShapeUp is also presented as the static precursor to ReShape. In both, the details of wiring and signaling are hidden from view, via metadata associated with individual modules. ReShape allows system reconfiguration at the module level, by supporting type checking of replacement modules and by managing the overall system implementation, via metadata associated with its FPGA floorplan. The methodology and tools have been implemented in a prototype for a broad domain-specific setting – networking systems – and have been validated on real telecommunications design projects

An end-user platform for FPGA-based design and rapid prototyping of feedforward artificial neural networks with on-chip backpropagation learning

The hardware implementation of an artificial neural network (ANN) using field-programmable gate arrays (FPGAs) is a research field that has attracted much interest and attention. With the developments made, the programmer is now forced to face various challenges, such as the need to master various complex hardware-software development platforms, hardware description languages, and advanced ANN knowledge. Moreover, such an implementation is very time consuming. To address these challenges, this paper presents a novel neural design methodology using a holistic modeling approach. Based on the end-user programming concept, the presented solution empowers end users by means of abstracting the low-level hardware functionalities, streamlining the FPGA design process and supporting rapid ANN prototyping. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. The recognition rate versus training data features and data representation was analyzed extensively

A scalable packetised radio astronomy imager

Includes bibliographical referencesModern radio astronomy telescopes the world over require digital back-ends. The complexity of these systems depends on many site-specific factors, including the number of antennas, beams and frequency channels and the bandwidth to be processed. With the increasing popularity for ever larger interferometric arrays, the processing requirements for these back-ends have increased significantly. While the techniques for building these back-ends are well understood, every installation typically still takes many years to develop as the instruments use highly specialised, custom hardware in order to cope with the demanding engineering requirements. Modern technology has enabled reprogrammable FPGA-based processing boards, together with packet-based switching techniques, to perform all the digital signal processing requirements of a modern radio telescope array. The various instruments used by radio telescopes are functionally very different, but the component operations remain remarkably similar and many share core functionalities. Generic processing platforms are thus able to share signal processing libraries and can acquire different personalities to perform different functions simply by reprogramming them and rerouting the data appropriately. Furthermore, Ethernet-based packet-switched networks are highly flexible and scalable, enabling the same instrument design to be scaled to larger installations simply by adding additional processing nodes and larger network switches. The ability of a packetised network to transfer data to arbitrary processing nodes, along with these nodes' reconfigurability, allows for unrestrained partitioning of designs and resource allocation. This thesis describes the design and construction of the first working radio astronomy imaging instrument hosted on Ethernet-interconnected re- programmable FPGA hardware. I attempt to establish an optimal packetised architecture for the most popular instruments with particular attention to the core array functions of correlation and beamforming. Emphasis is placed on requirements for South Africa's MeerKAT array. A demonstration system is constructed and deployed on the KAT-7 array, MeerKAT's prototype. This research promises reduced instrument development time, lower costs, improved reliability and closer collaboration between telescope design teams

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.Comment: Contribution to Snowmass 202