Browsing by Author "Joshi, Sanket Ramesh"

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    HBONext: An Efficient Dnn for Light Edge Embedded Devices
    (2021-05) Joshi, Sanket Ramesh; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    Every year the most effective Deep learning models, CNN architectures are showcased based on their compatibility and performance on the embedded edge hardware, especially for applications like image classification. These deep learning models necessitate a significant amount of computation and memory, so they can only be used on high-performance computing systems like CPUs or GPUs. However, they often struggle to fulfill portable specifications due to resource, energy, and real-time constraints. Hardware accelerators have recently been designed to provide the computational resources that AI and machine learning tools need. These edge accelerators have high-performance hardware which helps maintain the precision needed to accomplish this mission. Furthermore, this classification dilemma that investigates channel interdependencies using either depth-wise or group-wise convolutional features, has benefited from the inclusion of Bottleneck modules. Because of its increasing use in portable applications, the classic inverted residual block, a well-known architecture technique, has gotten more recognition. This work takes it a step forward by introducing a design method for porting CNNs to lowresource embedded systems, essentially bridging the difference between deep learning models and embedded edge systems. To achieve these goals, we use closer computing strategies to reduce the computer’s computational load and memory usage while retaining excellent deployment efficiency. This thesis work introduces HBONext, a mutated version of Harmonious Bottlenecks (DHbneck) combined with a Flipped version of Inverted Residual (FIR), which outperforms the current HBONet architecture in terms of accuracy and model size miniaturization. Unlike the current definition of inverted residual, this FIR block performs identity mapping and spatial transformation at its higher dimensions. The HBO solution, on the other hand, focuses on two orthogonal dimensions: spatial (H/W) contraction-expansion and later channel (C) expansion-contraction, which are both organized in a bilaterally symmetric manner. HBONext is one of those versions that was designed specifically for embedded and mobile applications. In this research work, we also show how to use NXP Bluebox 2.0 to build a real-time HBONext image classifier. The integration of the model into this hardware has been a big hit owing to the limited model size of 3 MB. The model was trained and validated using CIFAR10 dataset, which performed exceptionally well due to its smaller size and higher accuracy. The validation accuracy of the baseline HBONet architecture is 80.97%, and the model is 22 MB in size. The proposed architecture HBONext variants, on the other hand, gave a higher validation accuracy of 89.70% and a model size of 3.00 MB measured using the number of parameters. The performance metrics of HBONext architecture and its various variants are compared in the following chapters.
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    HBONext: HBONet with Flipped Inverted Residual
    (IEEE Xplore, 2021-06) Joshi, Sanket Ramesh; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    The top-performing deep CNN (DCNN) architectures are presented every year based on their compatibility and performance ability on the embedded edge applications, significantly for image classification. There are many obstacles in making these neural network architectures hardware friendly due to the limited memory, lesser computational resources, and the energy requirements of these devices. The addition of Bottleneck modules has further helped this classification problem, which explores the channel interdependencies, using either depthwise or groupwise convolutional features. The classical inverted residual block, a well-known design methodology, has now gained more attention due to its growing popularity in portable applications. This paper presents a mutated version of Harmonious Bottlenecks (DHbneck) with a Flipped version of Inverted Residual (FIR), which outperforms the existing HBONet architecture by giving the best accuracy value and the miniaturized model size. This FIR block performs identity mapping and spatial transformation at its higher dimensions, unlike the existing concept of inverted residual. The devised architecture is tested and validated using CIFAR-10 public dataset. The baseline HBONet architecture has an accuracy of 80.97% when tested on CIFAR-10 dataset and the model's size is 22 MB. In contrast, the proposed architecture HBONext has an improved validation accuracy of 88.30% with a model reduction to a size of 7.66 MB.