Exploring Versatility of Primary Visual Cortex Inspired Feature Extraction Hardware Model through Various Network Architectures

Thi Diem Tran, Y. Nakashima
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引用次数: 1

Abstract

Improving the performance of the network architectures that mimic brain operation is a research trend. Optimizing the latency on hardware circuits of the artificial neural network continues investigating. In the third generation, the Spiking Neural Networks (SNNs) with biological plausibility and similarity to the functionality of the human brain are emerging. A more comprehensive study is expected to understand the inherent behavior of SNNs, especially under adversarial attacks. This study concatenates the proposed SLIT layer with the convolutional neural networks (CNNs) to degrade the latency of deep neural networks on the hardware platform. The input data modified with the SLIT layer is applied to interrogate the adversarial attack on Spiking Neural Network. We estimate new topology with MNIST and CIFAR-10 datasets. Latency of the inference phase on CNNs for image classification application is assessed on the chip ZC7Z020-1CLG484C FPGA. Reducing latency in the range of 2.6% to 16% is observed from the Vitis AI platform. With white-box adversarial attack applications on SNNs, the accuracy of the proposal is approximately 70% higher robustness than the previous works.
基于不同网络架构的初级视觉皮层特征提取硬件模型的多功能性探索
提高模拟大脑操作的网络结构的性能是一个研究趋势。优化人工神经网络硬件电路的延迟仍在继续研究。在第三代,具有生物学合理性和与人类大脑功能相似的峰值神经网络(SNNs)正在出现。期望有更全面的研究来了解snn的固有行为,特别是在对抗性攻击下。本研究将所提出的SLIT层与卷积神经网络(cnn)相连接,以降低深度神经网络在硬件平台上的延迟。将经过SLIT层修改的输入数据应用于脉冲神经网络的对抗性攻击。我们使用MNIST和CIFAR-10数据集估计新的拓扑。在芯片ZC7Z020-1CLG484C FPGA上对cnn图像分类应用中的推理阶段延迟进行了评估。从Vitis AI平台观察到,延迟减少了2.6%至16%。对于snn上的白盒对抗性攻击应用,该建议的准确性比以前的工作高出约70%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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