Single-poly floating-gate memory cell options for analog neural networks

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In this paper, we explore the use of a 180 nm CMOS single-poly technology platform for realizing analog Deep Neural Network integrated circuits. The analysis focuses on analog vector–matrix multiplier architectures, one of the main building blocks of a neural network, implementing in-memory computation using Floating-Gate multi-level non-volatile memories. We present two memory options, suited either for current-mode or for time-domain vector–matrix multiplier implementations, with low–voltage charge-injection program and erase operations. The effects of a limited accuracy are also investigated through system-level simulations, by accounting for the temperature dependence of the stored weights and the corresponding impact on the network error rate.