医学应用中二值化神经网络的内存电阻式RAM实现
In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications
摘要：
深度学习的出现大大加速了机器学习的发展。然而，在边缘部署深度神经网络受到其高内存和能耗要求的限制。随着新的内存技术的出现，新兴的二值化神经网络（BNN）有望减少即将到来的机器学习硬件一代的能源影响，使机器学习在边缘设备上，并避免通过网络传输数据。在这项工作中，提出了我们的实现采用混合CMOS -氧化铪电阻存储器技术后，我们建议的策略，将BNN应用于生物医学信号，如心电图和脑电图，保持精度水平，减少内存需求。我们研究了二进制化整个网络和二进制化单独的分类器部分时的内存精度权衡。我们还讨论了如何将这些结果转化为Imagenet任务上的面向边缘的Mobilenet~V1神经网络。这项研究的最终目标是实现智能自主医疗设备。
英文摘要：
The advent of deep learning has considerably accelerated machine learning development. The deployment of deep neural networks at the edge is however limited by their high memory and energy consumption requirements. With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling machine learning on the edge devices and avoiding data transfer over the network. In this work, after presenting our implementation employing a hybrid CMOS - hafnium oxide resistive memory technology, we suggest strategies to apply BNNs to biomedical signals such as electrocardiography and electroencephalography, keeping accuracy level and reducing memory requirements. We investigate the memory-accuracy trade-off when binarizing whole network and binarizing solely the classifier part. We also discuss how these results translate to the edge-oriented Mobilenet~V1 neural network on the Imagenet task. The final goal of this research is to enable smart autonomous healthcare devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
