anerisheth19/CNN-MobileNet-V1-implementation-on-AWS-FPGA-using-OpenCL

Increasing the accuracy of Convolutional Neural Networks (CNNs) has become a recent research focus in computer vision applications. Smaller CNN architectures like SqueezeNet and MobileNet can demonstrate accelerated performance on FPGAs and GPUs due to smaller model size and fewer network parameters. Implementation of CNNs on accelerators have two important benefits - GPUs provide thread-level parallelism to achieve higher throughput and FPGAs offer a customizable application-specific datapath. These two reasons make these platforms better suited for convolution like operations which involve huge data. This project aims to implement one such CNN architecture, MobileNet on an Image dataset in OpenCL, thereby comparing kernel execution time and memory bandwidth usage on FPGA and GPU

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