HW/SW Co-Design to Configure DNN Models On Energy Harvesting Devices


New technical paper titled “EVE: Environmental Adaptive Neural Network Models for Low-Power Energy Harvesting System” was published by researchers at UT San Antonio, University of Connecticut, and Lehigh University.

According to the abstract:

“This paper proposes EVE, an automated machine learning (autoML) co-exploration framework to search for desired multi-models with shared weights for energy harvesting IoT devices. Those shared models incur significantly reduced memory footprint with different levels of model sparsity, latency, and accuracy to adapt to the environmental changes. An efficient on-device implementation architecture is further developed to efficiently execute each model on device. A run-time model extraction algorithm is proposed that retrieves individual model with negligible overhead when a specific model mode is triggered. Experimental results show that the neural networks models generated by EVE is on average 2.5X times faster than the baseline models without pruning and shared weights.”

Find the technical paper here. Published July 2022.

Authors: Sahidul Islam, Shanglin Zhou, Ran Ran, Yufang Jin, Wujie Wen, Caiwen Ding, Mimi Xie

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