SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

AbdelRahman Saeed, Ayman Tawfik, Hassan Mostafa, A. Khalil
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Abstract

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field Programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).
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基于机器学习的乳腺癌分类算法的soc实现
卷积神经网络(CNN)已经引起了医学成像领域研究人员的关注。许多研究人员利用CNN来检测乳腺癌。本研究提供了一种物联网(IoT)友好的CNN用于乳腺癌检测。为了加快产品上市速度,CNN的硬件实现采用了现场可编程门阵列(FPGA)上的深度学习处理单元(Deep-learning Processing Unit, DPU)。与传统的通用多核中央处理器(CPU)相比,该系统的CNN推理实现了1.6倍的加速系数和91.5%的能耗降低。
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