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A programmable imager for very high speed cellular signal processing

机译:用于高速蜂窝信号处理的可编程成像器

摘要

In this paper a programmable imager with averaging will be described which is intended for averaging of different groups or sets of pixels formed by n×n kernels, n×m kernels or independent pixels of the array. This imager is a 64×64 array which uses passive pixels that can be randomly accessed. The read-out stage includes a sole charge amplifier with programmable gain, a sample-and-hold structure and an analog buffer. This readout structure is different from other existing imagers with variable resolution since it uses a sole charge amplifier, whereas the normal structure is an operational amplifier per column plus a global operational amplifier. This structure will be described in detail indicating the advantages and disadvantages with respect to other imagers with averaging capabilities. This programmable resolution architecture can be more appropriate, and eventually, more efficient, when implementing very high speed Cellular Neural Network (CNN) processors in a CNN chipset - a mixed-signal hardware platform for CNN-based image processing. A significant processing time reduction can be obtained when decreasing the image resolution, and therefore the amount of information to be transferred to the CNN processor. This programmable resolution can also be used for fast image recognition and ulterior windowing at full resolution in a reduced area of the image, permitting a more accurate processing of the region of interest. In addition, full resolution images can still be obtained, as in commercial imagers which are usually included in CNN chipsets.
机译:在本文中,将描述具有平均的可编程成像器,该成像器旨在平均由阵列的n×n个核,n×m个核或独立像素形成的不同像素组或像素集。该成像器是一个64×64阵列,它使用可以随机访问的无源像素。读出级包括一个具有可编程增益的唯一电荷放大器,一个采样保持结构和一个模拟缓冲器。这种读出结构与其他现有的具有可变分辨率的成像器不同,因为它使用唯一的电荷放大器,而正常的结构是每列运算放大器加全局运算放大器。将详细描述该结构,以指示相对于具有平均能力的其他成像器的优点和缺点。当在CNN芯片组(用于基于CNN的图像处理的混合信号硬件平台)中实现超高速蜂窝神经网络(CNN)处理器时,这种可编程的分辨率体系结构可能会更合适,最终会更高效。当降低图像分辨率时,可以显着减少处理时间,从而减少要传输到CNN处理器的信息量。该可编程分辨率还可以用于在图像的缩小区域中以全分辨率进行快速图像识别和别有用处的窗口显示,从而可以对目标区域进行更精确的处理。此外,仍然可以获取全分辨率图像,就像通常包含在CNN芯片集中的商业成像仪中那样。

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