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Deep Neural Network Model Development using Machine Learning for a Semiconductor Manufacturer in the US

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Overview

The client is a prominent semiconductor manufacturer headquartered in the US, specializing in the production of microcontrollers, processors, sensors, analog integrated circuits (ICs), and connectivity solutions. They wanted a technology partner to help them in proving the application of their newly developed chipsets for Machine Learning at edge to be launched in the market. ACL Digital designed the handwritten digit recognition application using powerful supervised deep learning technique – Convolution Neural Network (CNN) and developed developed Caffe model using AlexNet architecture and trained using MNIST database of 50,000 images with accelerated launch timeline by 30%.

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    Challenges

    Absence of hand-written digit recognition application on the newly developed chipsets

    Outcomes

    Benefits Hand written Digit Recognition and Classification on Edge
    Benefits Hand written Digit Recognition and Classification on Edge
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