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Analog Convolutional Neural Network

Convolutional neural networks (CNNs) have become the mainstay of machine learning. CNNs are the digital signal processing equivalent of the multiply and add DSP instruction. The backpropagation algorithm originated in the late 1980’s. The combination of backpropagation and high computational power of graphics processing units (GPUs) has enabled dramatic, exponential growth in deep convolutional neural networks. We will revisit analog signal processing from the 1970’s and illustrate how analog computation can be applied to convolutional neural networks. Convolutional neural networks need to apply the exponential gains from CMOS digital circuits to low power analog signal processing: analog multipliers, adders, and nonlinear functions. A design concept is presented for an analog convolutional network embedded inside a charge-coupled device (CCD) camera. The paper closes with a simulation of the proposed concept. Index terms — Convolutional Neural Network, CNN, analog signal processing, CCD, and low power.