bharathsudharsan/ML-Classifiers-on-MCUs
Supplementary material for IEEE Services Computing paper 'An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware'
This project helps embedded systems engineers and researchers deploy machine learning classifiers onto very small, memory-constrained hardware like microcontrollers. It takes pre-trained machine learning models and optimizes them to run with minimal memory and maximum speed, even on tiny devices. The result is efficient, on-device intelligence for applications where power and memory are critical.
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Use this if you need to run machine learning models directly on low-power, resource-limited microcontrollers or embedded systems.
Not ideal if you are working with cloud-based ML inference or have ample memory and processing power available on your target hardware.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 26, 2021
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