混合信号CNN阵列芯片用于图像处理

Á. Rodríguez-Vázquez, S. E. Meana, R. Domínguez-Castro, R. Carmona, E. Roca
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引用次数: 8

摘要

由于其局部连接性和广泛的功能能力,细胞非线性网络(CNN)是使用VLSI模拟并行阵列实现图像处理算法的绝佳候选者。然而,设计具有实际应用所需尺寸的通用可编程CNN芯片给模拟设计人员带来了许多具有挑战性的问题。这主要是由于硅面积大意味着开发成本大,设计参数的空间偏差大,生产良率低。CNN设计人员在设计大型CNN芯片时,要保持足够合理的精度水平和成品率以及合理的低开发成本,必须面对不同的问题。本文概述了其中的一些主要问题及其解决方案。
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Mixed-signal CNN array chips for image processing
Due to their local connectivity and wide functional capabilities, cellular nonlinear networks (CNN) are excellent candidates for the implementation of image processing algorithms using VLSI analog parallel arrays. However, the design of general purpose, programmable CNN chips with dimensions required for practical applications raises many challenging problems to analog designers. This is basically due to the fact that large silicon area means large development cost, large spatial deviations of design parameters and low production yield. CNN designers must face different issues to keep reasonable enough accuracy level and production yield together with reasonably low development cost in their design of large CNN chips. This paper outlines some of these major issues and their solutions.
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