Algorithmic methodologies for ultra-efficient inexact architectures for sustaining technology scaling

L. Avinash, Kirthi Krishna Muntimadugu, C. Enz, R. Karp, K. Palem, C. Piguet
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引用次数: 56

Abstract

Owing to a growing desire to reduce energy consumption and widely anticipated hurdles to the continued technology scaling promised by Moore's law, techniques and technologies such as inexact circuits and probabilistic CMOS (PCMOS) have gained prominence. These radical approaches trade accuracy at the hardware level for significant gains in energy consumption, area, and speed. While holding great promise, their ability to influence the broader milieu of computing is limited due to two shortcomings. First, they were mostly based on ad-hoc hand designs and did not consider algorithmically well-characterized automated design methodologies. Also, existing design approaches were limited to particular layers of abstraction such as physical, architectural and algorithmic or more broadly software. However, it is well-known that significant gains can be achieved by optimizing across the layers. To respond to this need, in this paper, we present an algorithmically well-founded cross-layer co-design framework (CCF) for automatically designing inexact hardware in the form of datapath elements. Specifically adders and multipliers, and show that significant associated gains can be achieved in terms of energy, area, and delay or speed. Our algorithms can achieve these gains with adding any additional hardware overhead. The proposed CCF framework embodies a symbiotic relationship between architecture and logic-layer design through the technique of probabilistic pruning combined with the novel confined voltage scaling technique introduced in this paper, applied at the physical layer. A second drawback of the state of the art with inexact design is the lack of physical evidence established through measuring fabricated ICs that the gains and other benefits that can be achieved are valid. Again, in this paper, we have addressed this shortcoming by using CCF to fabricate a prototype chip implementing inexact data-path elements; a range of 64-bit integer adders whose outputs can be erroneous. Through physical measurements of our prototype chip wherein the inexact adders admit expected relative error magnitudes of 10% or less, we have found that cumulative gains over comparable and fully accurate chips, quantified through the area-delay-energy product, can be a multiplicative factor of 15 or more. As evidence of the utility of these results, we demonstrate that despite admitting error while achieving gains, images processed using the FFT algorithm implemented using our inexact adders are visually discernible.
用于维持技术扩展的超高效非精确架构的算法方法
由于降低能耗的愿望日益增长,以及摩尔定律所承诺的持续技术扩展的广泛预期障碍,诸如不精确电路和概率CMOS (PCMOS)等技术和技术已获得突出地位。这些激进的方法以硬件级别的准确性为代价,在能耗、面积和速度方面取得了显著的进步。虽然前景光明,但由于两个缺点,它们影响更广泛计算环境的能力受到限制。首先,它们大多基于特别的手工设计,没有考虑算法上特征良好的自动化设计方法。此外,现有的设计方法仅限于特定的抽象层,如物理、架构和算法或更广泛的软件。然而,众所周知,通过跨层优化可以获得显著的收益。为了满足这一需求,在本文中,我们提出了一个基于算法的跨层协同设计框架(CCF),用于以数据路径元素的形式自动设计不精确的硬件。特别是加法器和乘法器,并表明在能量、面积、延迟或速度方面可以实现显著的相关增益。我们的算法可以在不增加任何额外硬件开销的情况下实现这些增益。本文提出的CCF框架通过概率剪枝技术结合本文介绍的新型限压标度技术,在物理层应用,体现了架构与逻辑层设计之间的共生关系。不精确设计的第二个缺点是缺乏通过测量制造的集成电路来建立的物理证据,证明可以实现的增益和其他好处是有效的。同样,在本文中,我们通过使用CCF制造实现不精确数据路径元素的原型芯片来解决这一缺点;一组64位整数加法器,其输出可能是错误的。通过对我们的原型芯片的物理测量,其中不精确加法器承认预期的相对误差幅度为10%或更小,我们发现,通过面积延迟能量积量化,与可比和完全精确的芯片相比,累积增益可以是15或更多的乘法因子。作为这些结果的实用性的证据,我们证明,尽管在获得增益的同时承认错误,但使用我们的不精确加法器实现的FFT算法处理的图像在视觉上是可识别的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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