  IoT devices are increasingly being implemented with neural network models to
enable smart applications. Energy harvesting (EH) technology that harvests
energy from ambient environment is a promising alternative to batteries for
powering those devices due to the low maintenance cost and wide availability of
the energy sources. However, the power provided by the energy harvester is low
and has an intrinsic drawback of instability since it varies with the ambient
environment. 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.
