2018 · 19 citations · 11 references
EngineeringComputer EngineeringComputer ScienceComputer VisionField-programmable Gate ArraysReconfigurable ComputingAccelerator ArchitectureFpga ArchitectureHardware AccelerationImage ProcessingNeural NetworksPattern RecognitionConvolutional Neural NetworksDeep Learning AccelerationDeep Learning AcceleratorsDeep Learning-based ClassificationImage Processing AcceleratorsImage Processing UnitsMedical Image ClassificationReconfigurable Hardware Architecture
Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex and less accurate. An accurate, simple and hardware efficient architecture is required to be developed for image classification. In this paper, Convolutional Neural Network (CNN) architecture has been proposed and validated using MNIST handwritten dataset. The adopted approaches of sliding-filter for convolution and parallel computation of Multiplication and Accumulation (MAC) operations resulted in optimized hardware architecture with reduced arithmetic operations and faster computations. The developed architecture has been implemented on Artix-7 FPGA and attained a significant improvement in speed compared to existing architecture working at 300MHz maximum operating frequency.
11
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
Chen Zhang, Peng Li, Guangyu Sun et al. · 2015 · 2.1K citations