Deep Neural Network Model Development using Machine Learning for a Semiconductor Manufacturer in the US
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
Solution
- Migration of legacy application and database from legacy cloud to ClearBlade cloud
- MQTT support with faster response and throughput, lower battery and bandwidth usage
- Developed Java-based Wi-Fi adapter to connect legacy devices to new ClearBlade platform
- Developed web portal for end users and admin for system maintenance
- Enabling device control using Google Home and Amazon Alexa smart home skills
- Mobile app with dynamic UI/UX, MQTT support, new features implementation to control all the thermostats using mobile app
Outcomes
- Accelerated client’s product launch timeline by 30% with hands-on experience in machine learning domain
