高精度容错随机内产品设计

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摘要翻译：
在本工作中，我们提出了一个新的随机计算内积设计。随机计算是一种新兴的计算技术，它在随机比特流中以观察到一个数字的概率对一个数字进行编码。这导致了硬件成本的降低和高容错性。提出的内积设计基于双线双极性编码格式，并在中央累加单元中应用输入的顺序处理。顺序处理大大提高了计算精度，因为它允许进位的初步消除。此外，与传统的加法器树方法相比，中心累加单元具有更好的可扩展性。我们表明，在高精度要求和容错性方面，所提出的内部产品设计在硬件成本方面优于现有的设计。
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英文标题：
《High-Accuracy and Fault Tolerant Stochastic Inner Product Design》
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作者：
Werner Haselmayr, Daniel Wiesinger, Michael Lunglmayr
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Emerging Technologies        新兴技术
分类描述：Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
涵盖基于硅CMOS技术替代品的信息处理（计算、通信、传感）和生物化学分析方法，如纳米级电子、光子、自旋、超导、机械、生物化学和量子技术（此列表不是唯一的）。感兴趣的主题包括：（1）新兴技术的构建块、其可伸缩性和在大型系统中的采用，包括与传统技术的集成；（2）新型设备和系统的建模、设计和优化；（3）新兴技术的计算模型、算法设计和编程。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In this work, we present a novel inner product design for stochastic computing. Stochastic computing is an emerging computing technique, that encodes a number in the probability of observing a one in a random bit stream. This leads to reduced hardware costs and high error tolerance. The proposed inner product design is based on a two-line bipolar encoding format and applies sequential processing of the input in a central accumulation unit. Sequential processing significantly increases the computation accuracy, since it allows for preliminary cancelation of carry bits. Moreover, the central accumulation unit gives a much better scalability compared to conventional adder tree approaches. We show that the proposed inner product design outperforms state-of-the-art designs in terms of hardware costs for high accuracy requirements and fault tolerance.
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PDF链接：
https://arxiv.org/pdf/1808.065

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