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Work-in-Progress: Quantized NNs as the Definitive Solution for Inference on Low-Power ARM MCUs?
2018 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)
High energy efficiency and low memory footprint are the key requirements for the deployment of deep learning based analytics on low-power microcontrollers. Here we present work-in-progress results with Q-bit Quantized Neural Networks (QNNs) deployed on a commercial Cortex-M7 class microcontroller by means of an extension to the ARM CMSIS-NN library. We show that i) for Q = 4 and Q = 2 low memory footprint QNNs can be deployed with an energy overhead of 30% and 36% respectively against the 8-bitdoi:10.1109/codesisss.2018.8525915 dblp:conf/codes/RusciC0B18 fatcat:yicgyf5vnbesvibkr33jdcmrcy