模拟深度二值化神经网络的快速仿真方法

2019 International SoC Design Conference (ISOCC) Pub Date : 2019-10-06 DOI:10.1109/ISOCC47750.2019.9078516

Chaeun Lee, Jaehyun Kim, Jihun Kim, Jaehyun Kim, Cheol Seong Hwang, Kiyoung Choi

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引用次数: 0

摘要

提出了一种模拟深度二值化神经网络的仿真方法，可以实现快速、准确的仿真。该方法基于查找表，可以在GPU上加速仿真。它使用SPICE等电路模拟器在各种环境下提取查找表。为了证明该方法的有效性，我们给出了模拟深度二值化神经网络的实验结果。在实验中，与FineSim在多层感知器上的仿真相比，我们可以将仿真速度提高612K倍。

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Fast Simulation Method for Analog Deep Binarized Neural Networks

We propose a simulation method for analog deep binarized neural networks which enables fast and accurate simulation. This method is based on look-up tables and can accelerate simulation on a GPU. It extracts the look-up tables using a circuit simulator such as SPICE under various types of environments. To prove the validity of this method, we show the experimental results for analog deep binarized neural networks. In the experiment, we could accelerate the simulation by 612K times compared to FineSim simulation on an example of multilayer perceptron.

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2019 International SoC Design Conference (ISOCC)

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