English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

IoT devices are increasingly being implemented with neural network models toenable smart applications. Energy harvesting (EH) technology that harvestsenergy from ambient environment is a promising alternative to batteries forpowering those devices due to the low maintenance cost and wide availability ofthe energy sources. However, the power provided by the energy harvester is lowand has an intrinsic drawback of instability since it varies with the ambientenvironment. This paper proposes EVE, an automated machine learning (autoML)co-exploration framework to search for desired multi-models with shared weightsfor energy harvesting IoT devices. Those shared models incur significantlyreduced memory footprint with different levels of model sparsity, latency, andaccuracy to adapt to the environmental changes. An efficient on-deviceimplementation architecture is further developed to efficiently execute eachmodel on device. A run-time model extraction algorithm is proposed thatretrieves individual model with negligible overhead when a specific model modeis triggered.Experimental results show that the neural networks modelsgenerated by EVE is on average 2.5X times faster than the baseline modelswithout pruning and shared weights.

EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System