一种调度和绑定启发式算法,用于高级容错FPGA应用的综合

Aniruddha Shastri, G. Stitt, Eduardo Riccio
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引用次数: 12

摘要

空间计算系统通常使用现场可编程门阵列,通过对现有的寄存器-传输级(RTL)代码应用三模冗余(TMR)来提供容错能力。虽然这种方法是有效的,但它有3倍的面积开销,这对于经常在考虑冗余影响之前分配资源的许多设计来说可能是令人望而却步的。尽管设计人员可以修改现有的RTL代码以减少资源使用,但这样的过程既耗时又容易出错。将冗余集成到高级合成中是一种更有吸引力的方法,它使合成能够快速探索不同的权衡,而不需要设计者付出任何代价。在本文中,我们引入了一种用于高级综合的调度和绑定启发式方法,该方法探索了资源使用、延迟和冗余量之间的权衡。在许多情况下,应用程序不需要100%的错误纠正,这为调度和绑定提供了极大的灵活性,从而减少了资源。即使对于需要100%纠错的应用程序,我们的启发式方法也能够探索牺牲延迟以减少资源的解决方案,当将延迟放宽到2倍时,通常可以节省高达47%的时间。当误差约束减少到70%时,我们的启发式算法在将延迟放宽到2倍(最大为77%)时实现了典型的资源节省,范围从18%到49%。即使与优化后的RTL设计相比,我们的启发式算法使用的资源也比TMR少61%。
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A scheduling and binding heuristic for high-level synthesis of fault-tolerant FPGA applications
Space computing systems commonly use field-programmable gate arrays to provide fault tolerance by applying triple modular redundancy (TMR) to existing register-transfer-level (RTL) code. Although effective, this approach has a 3× area overhead that can be prohibitive for many designs that often allocate resources before considering effects of redundancy. Although a designer could modify existing RTL code to reduce resource usage, such a process is time consuming and error prone. Integrating redundancy into high-level synthesis is a more attractive approach that enables synthesis to rapidly explore different tradeoffs at no cost to the designer. In this paper, we introduce a scheduling and binding heuristic for high-level synthesis that explores tradeoffs between resource usage, latency, and the amount of redundancy. In many cases, an application will not require 100% error correction, which enables significant flexibility for scheduling and binding to reduce resources. Even for applications that require 100% error correction, our heuristic is able to explore solutions that sacrifice latency for reduced resources, and typically save up to 47% when relaxing the latency up to 2×. When the error constraint is reduced to 70%, our heuristic achieves typical resource savings ranging from 18% to 49% when relaxing the latency up to 2×, with a maximum of 77%. Even when comparing with optimized RTL designs, our heuristic uses up to 61% fewer resources than TMR.
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