Discriminatively Fortified Computing with Reconfigurable Digital Fabric

Mingjie Lin, Yu Bai, J. Wawrzynek
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引用次数: 4

Abstract

This work proposes a novel approach -- Discriminatively Fortified Computing (DFC) -- to achievehardware-efficient reliable computing without deterministically knowing the location and occurrence time of hardware defects and design faults. The key insights behind DFC comprise:1) different system components contribute differently to the overall correctness of a target application, therefore should be treated distinctively, and 2) abundant error resilience exists inherently in many practical algorithms, such as signal processing, visual perception, and artificial learning. Such error resilience can be significantly improved with effective hardware support. The major contributions of this work include 1) the development of a complete methodology to perform sensitivity and criticality analysis of hardware redundancy, 2) a novel problem formulation and an efficient heuristic methodology to discriminatively allocate hardware redundancy among a targetdesign's key components in order to maximize its overall error resilience, 3) an academic prototype of DFC computing device that illustrates a 4 times improvement of error resilience for aH.264 encoder implemented with an FPGA device.
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可重构数字结构的判别强化计算
这项工作提出了一种新的方法——判别强化计算(DFC)——在不确定性地知道硬件缺陷和设计故障的位置和发生时间的情况下实现硬件高效可靠计算。DFC背后的关键见解包括:1)不同的系统组件对目标应用程序的整体正确性贡献不同,因此应该区别对待;2)在许多实际算法中固有地存在丰富的错误弹性,例如信号处理、视觉感知和人工学习。通过有效的硬件支持,可以显著提高这种错误恢复能力。这项工作的主要贡献包括:1)开发了一种完整的方法来执行硬件冗余的敏感性和临界性分析;2)一种新的问题公式和一种有效的启发式方法,在目标设计的关键组件之间区分地分配硬件冗余,以最大限度地提高其整体错误弹性;3)DFC计算设备的学术原型,说明了aH的错误弹性提高了4倍。264编码器实现的FPGA设备。
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