Application Customisation: Enhancing Design Quality and Developer Productivity

自从冯·诺伊曼在1945年阐明了它们的基本原理，霍夫在1969年开发了微处理器体系结构以来，主流处理器体系结构中没有太多的变化。这种情况正在改变：现场可编程技术已被微软和英特尔等大公司用于数据中心计算，并有望在同一芯片上集成处理器核心和现场可编程资源的新体系结构。这些发展在很大程度上是由现场可编程技术在性能和能源效率方面的改进推动的，现场可编程技术如此前景光明，以至于尽管开发基于现场可编程技术的定制计算系统的应用程序存在巨大挑战，但工业应用仍在进行。我们的愿景是通过推进定制的基础和应用程序来应对这一挑战，定制包括开发满足设计要求的硬件和软件。拟议的平台项目旨在开创新的能力，以提高定制计算系统的设计质量和设计人员的生产力，有可能使许多应用程序发生革命性变化，包括那些需要大数据处理或提高可靠性和安全性的应用程序。它建立在我们以前的平台(EP/I012036/1)资助的颠覆性研究的成功的基础上。这种成功的例子是对定制计算系统的运行时重构的研究：我们开发了新的分析方法来使重构能够消除空闲功能；我们展示了重构如何使许多应用受益，例如基因组数据处理和有限差分计算。我们的工作具有颠覆性，因为与目前对部分重新配置的关注不同，它表明完全重新配置可以提供比传统多核和多核处理器显著的能效加速，例如，将亚硫酸盐序列比对的运行时间从几小时减少到几分钟，以进行非侵入性产前和癌症诊断。此外，我们发明了第一个能够生成单周期片上配置的现场可编程架构，而目前的商业设备都是基于可能需要几个小时的片外配置生成。这种令人兴奋的进展之所以可能，是因为Platform Grant使研究人员能够进行高风险的研究，否则他们将面临资金缺口：我们团队中的12名研究助理在找到永久职位之前享受了平台支持。更新的平台支持将使我们充满活力和雄心勃勃的研究团队继续发展，探索下一代计算机系统及其应用。更新的平台资助的灵活性将用于解决我们唯一有能力产生重大影响的三个新战略领域；我们将进行探索性研究，以确定响应模式或其他形式的资金的有前途的项目：1.多层次的权衡意识设计自动化，包括调查定制策略和相关的权衡，有效的定制策略的自动化，以及开发可重复使用的演示设施和测试台。可重构的大数据和云架构，包括可定制的大数据处理、运行时设计生成和优化以及特定领域的云优化。可靠的系统开发生命周期，其中包括可靠和有弹性的系统的联合设计、高覆盖率的测试和验证战略以及可靠性和弹性生命周期管理。这项平台赠款提案的增值方面包括：(A)确保关键领域的研究人员达到临界数量，(B)探索重要的战略领域，(C)为研究基础设施做出贡献，(D)吸引新的人才，(E)开拓和加强国际合作，以及(F)加快技术转让。

There have not been many shake-ups in mainstream processor architectures, since von Neumann articulated their basic principles in 1945 and Hoff developed the microprocessor architecture in 1969. This is changing: field programmable technology has been adopted by major companies such as Microsoft and Intel for datacentre computing, and new architectures are expected which integrate processor cores and field programmable resources on the same chip. These developments are largely motivated by improvements in performance and energy efficiency of field programmable technology, which are so promising that industrial adoption takes place despite the significant challenge of developing applications for custom computing systems based on field programmable technology.Our vision is to address this challenge by advancing the foundation and applications of customisation, which involves developing hardware and software to fit design requirements. The proposed Platform project aims to pioneer new capabilities for enhancing design quality and designer productivity of custom computing systems, with potential to revolutionise many applications including those with needs for big data processing or for improved reliability and security. It builds on success of disruptive research funded by our previous Platform (EP/I012036/1).An example of such success is research in runtime reconfiguration of custom computing systems: we developed new analysis methods to enable reconfiguration to remove idle functions; we showed how reconfiguration can benefit many applications such as genomic data processing and finite-difference computation. Our work is disruptive since, in contrast to current focus on partial reconfiguration, it demonstrates that full reconfiguration can provide significant energy-efficient acceleration over conventional multicore and manycore processors reducing, for example, runtime of Bisulfite sequence alignment from hours to minutes for non-invasive prenatal and cancer diagnosis. Moreover, we invented the first field programmable architecture capable of single-cycle on-chip configuration generation, while current commercial devices are based on off-chip configuration generation that can take hours. Such exciting progress is only possible because the Platform Grant enabled high-risk research by researchers who would otherwise suffer from funding gaps: 12 Research Associates in our team enjoyed Platform support before they found permanent positions. Renewed Platform support will allow continuing development of our dynamic and ambitious research team to explore next-generation computer systems and their applications.The flexibility of the renewed Platform Grant will be used to address three new strategic areas, on which we are uniquely capable of making major impacts; we will conduct exploratory research to identify promising projects for responsive mode or other forms of funding:1. Multi-level tradeoff-aware design automation, which includes investigating customisation strategies and the associated tradeoffs, automation of effective customisation strategies, and developing reusable demonstration facilities and testbeds.2. Reconfigurable big data and cloud architectures, which include customisable big data processing, runtime design generation and optimisation, and domain-specific cloud optimisation.3. Reliable system development life cycle, which includes codesign of reliable and resilient systems, high-coverage testing and verification strategies, and reliability and resilience life cycle management.The added-value aspects for this Platform Grant proposal include: (a) ensuring a critical mass of researchers in key areas, (b) exploring significant strategic areas, (c) contributing to research infrastructure, (d) attracting fresh talents, (e) pioneering and strengthening international collaborations, and (f) accelerating technology transfer.