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SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: Demo abstract
(Association for Computing Machinery (ACM), 2021-05-19)
With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine ...
Ultra-fast machine learning classifier execution on IoT devices without SRAM consumption
(Institute of Electrical and Electronics Engineers (IEEE), 2021-05-25)
With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be ...
An SRAM optimized approach for constant memory consumption and ultra-fast execution of ML classifiers on TinyML hardware
(Institute of Electrical and Electronics Engineers, 2021-11-15)
With the introduction of ultra-low-power machine
learning (TinyML), IoT devices are becoming smarter as they are
driven by Machine Learning (ML) models. However, any increase
in the training data results in a linear ...