摘要翻译：
一般的设计自动化，特别是逻辑综合，可以在实现应用特定的二值化神经网络(BNN)的设计中发挥关键作用。本文介绍了一种用于超低功耗近传感器处理的纯组合BNN的硬件设计与综合。我们利用BNN模型带来的主要机会，将超低功耗深度学习电路推向传感器附近，并将其与二值化混合信号图像传感器数据耦合。BNN模型主要由逻辑比特操作、整数计数和比较组成。我们分析了作为组合网络的BNN的面积、功率和能量度量。我们在GlobalFoundries 22nm SOI技术上的合成结果显示，用于实现32×32二进制输入传感器感受野和在设计时固定重量参数的组合BNN的硅面积为2.61mm2。这比具有可重新配置参数的合成网络小2.2倍。与深度学习近传感器处理的其他可比技术相比，我们的方法具有10倍的能效。
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英文标题：
《Design Automation for Binarized Neural Networks: A Quantum Leap
  Opportunity?》
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作者：
Manuele Rusci, Lukas Cavigelli, Luca Benini
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Other Computer Science        其他计算机科学
分类描述：This is the classification to use for documents that do not fit anywhere else.
这是用于不适合其他任何地方的文档的分类。
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一级分类：Computer Science        计算机科学
二级分类：Hardware Architecture        硬件体系结构
分类描述：Covers systems organization and hardware architecture. Roughly includes material in ACM Subject Classes C.0, C.1, and C.5.
涵盖系统组织和硬件架构。大致包括ACM主题课程C.0、C.1和C.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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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：
  Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper presents the hardware design and synthesis of a purely combinational BNN for ultra-low power near-sensor processing. We leverage the major opportunities raised by BNN models, which consist mostly of logical bit-wise operations and integer counting and comparisons, for pushing ultra-low power deep learning circuits close to the sensor and coupling it with binarized mixed-signal image sensor data. We analyze area, power and energy metrics of BNNs synthesized as combinational networks. Our synthesis results in GlobalFoundries 22nm SOI technology shows a silicon area of 2.61mm2 for implementing a combinational BNN with 32x32 binary input sensor receptive field and weight parameters fixed at design time. This is 2.2x smaller than a synthesized network with re-configurable parameters. With respect to other comparable techniques for deep learning near-sensor processing, our approach features a 10x higher energy efficiency.
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PDF链接：
https://arxiv.org/pdf/1712.01743