Abstract
Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement CNNs are increasing rapidly. This is due to the lower power consumption and easy reconfigurability that are offered by these platforms. Because of the research efforts put into topics, such as architecture, synthesis, and optimization, some new challenges are arising for integrating suitable hardware solutions to high-level machine learning software libraries. This paper introduces an integrated framework (CNN2Gate), which supports compilation of a CNN model for an FPGA target. CNN2Gate is capable of parsing CNN models from several popular high-level machine learning libraries, such as Keras, Pytorch, Caffe2, etc. CNN2Gate extracts computation flow of layers, in addition to weights and biases, and applies a “given” fixed-point quantization. Furthermore, it writes this information in the proper format for the FPGA vendor’s OpenCL synthesis tools that are then used to build and run the project on FPGA. CNN2Gate performs design-space exploration and fits the design on different FPGAs with limited logic resources automatically. This paper reports results of automatic synthesis and design-space exploration of AlexNet and VGG-16 on various Intel FPGA platforms.
Highlights
The impact of machine learning and deep learning is rapidly growing in our society, due to their diverse technological advantages
The first downside of using graphic processing units (GPUs) is their high power consumption. This makes GPUs hard to use in robotics, drones, self-driving cars, and Internet of Things (IoTs), while these fields can highly benefit from deep learning algorithms
The present paper proposes methods for tackling research challenges that are related to the integration of high-level synthesis tools for convolutional neural networks on Field Programmable Gate Arrays (FPGA)
Summary
The impact of machine learning and deep learning is rapidly growing in our society, due to their diverse technological advantages. Convolutional neural networks (CNNs) are among the most notable architectures that provide a very powerful tool for many applications, such as video and image analysis, speech recognition, and recommender systems [1]. The first downside of using GPUs is their high power consumption. This makes GPUs hard to use in robotics, drones, self-driving cars, and Internet of Things (IoTs), while these fields can highly benefit from deep learning algorithms. A CNN consists of one or several convolutional, pooling, and fully connected layers. These layers can be connected together to shape a deep convolutional neural network model. The data enters from the input, is processed by various hidden layers, and the classification results are presented as outputs
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