US based leading semiconductor manufacturer offering microcontrollers and processors for sensors, analog ICs and connectivity.
- The client was looking for a technology partner to help them in proving the application of newly developed chipsets for Machine Learning at edge to be launched in the market
- Designed the handwritten digit recognition application using powerful supervised deep learning technique – Convolution Neural Network (CNN)
- Developed Caffe model using AlexNet architecture and trained using MNIST database of 50,000 images
- Developed script in Python to export model parameter & converted using CMSIS-NN library to deploy on edge – i.MXRT1062
- Developed application in C on NXP i.MXRT1062 for capturing the image, pre-processing and executing deep learning model for digit classification
- Analyzed the performance of the model by tweaking different parameters and layers of the AlexNet architecture
- Performed testing of the model with 10,000+ images
- Accelerated client’s product launch timeline by 30% with hands-on experience in machine learning domain
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