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Abstract-Caffe is a deep learning framework, originally developed at UC Berkeley and widely used in large-scale industrial applications such as vision, speech, and multimedia. It supports many different types of deep learning architectures such as CNNs (convolutional neural networks) geared towards image classification and image recognition. In this paper we develop a platform for the efficient deployment and acceleration of Caffe framework on embedded systems that are based on the Zynq SoC. The most computational intensive part of image classification is the processing of the convolution layers of the deep learning algorithms and more specifically the GEMM (general matrix multiplication) function calls. In the proposed framework, a hardware accelerator has been implemented, validated and optimized using Xilinx SDSoC Development Environment to perform the GEMM function. The accelerator that was developed achieves up to 98× speed-up compared with the simple ARM CPU implementation. The results showed that the mapping of Caffe on the FPGA-based Zynq takes advantage of the low-power, customizable and programmable fabric and ultimately reduces time and power consumption of image classification.

Cloud and Fog technologies are steadily gaining momentum and popularity in the research and industry circles. Both communities are wondering about the resource usage. The present work aims to predict the resource usage of a machine learning application in an edge environment, utilizing Raspberry Pies. It investigates various experimental setups and machine learning methods that are acting as benchmarks, allowing us to compare the accuracy of each setup. We propose a prediction model that leverages the time series characteristics of resource utilization employing an LSTM Recurrent Neural Network (LSTM-RNN). To conclude to a close to optimal LSTM-RNN architecture we use a genetic algorithm. For the experimental evaluation we used a real dataset constructed by training a well known model in Raspberry Pies3. The results encourage us for the applicability of our method.

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