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The growing need of fine-tuning large language models (LLMs) can lead to significant energy consumption and environmental impact. To address this issue, we introduce GreenTrainer, a novel LLM fine-tuning technique. GreenTrainer assesses the backpropagation costs and contributions of different tensors to model accuracy, allowing for the selection of the most efficient set of tensors. This selection is guided by a user-defined objective, which can adapt to energy supply considerations and Green AI goals. Experimental results demonstrate that GreenTrainer can reduce FLOPs by up to 64% without compromising model accuracy, and outperforms existing techniques like LoRA while maintaining comparable FLOPs reduction.

The first on-device AI technique that achieves full elasticity of on-device training on resource-constrained mobile and embedded devices. By leveraging the principle of eXplainable AI (XAI) and evaluating the importance of different tensors in training, we allow fully flexible adaptation of the trainable neural network portion at runtime, according to the current training needs and online data patterns, to minimize the training cost without accuracy loss.

The first mobile health system that turns a commodity smartphone into a fully functional pulmonary examination device to measure the internal physiological conditions of human airways, such as airway caliber, obstruction and possible inflammation. Information about these airway conditions could provide vital clues for precise and objective pulmonary disease evaluation.

This work applies on-device AI techniques to interference cancellation in WiFi networks and enables generalizable interference cancellation on commodity WiFi devices without any extra RF hardware. By using neural network models to mimic WiFi network’s PHY-layer operation, AiFi can be generally applied to different types of interference signals ranging from concurrent WiFi transmissions, ZigBee/Bluetooth to wireless baby monitors or even microwave oven, and improves the MAC-layer frame reception rate by 18x.

This work prsents AWARE, a new sensing and AI system that supports accurate and reliable pulmonary function tests (PFT) using commodity smartphones. AWARE uses a smartphone to transmit acoustic signals and reconstructs the profile of human airway based on the analysis of reflected acoustic waves captured from the smartphone’s microphone. The subject’s pulmonary condition is then evaluated by a multi-task learning model to detect airway obstruction and inflammation and identify pulmonary diseases like asthma or COPD.

AgileNN is the first work that achieves real-time inference (<20ms) of mainstream neural network models (e.g., ImageNet) on extremely weak MCUs (e.g., STM32 series with <1MB of memory), without impairing the inference accuracy. The usage of eXplainable AI (XAI) techniques allows >6x improvement of feature compressibility during offloading and >8x reduction of the local device’s resource consumption.

TransFi realizes fine-grained signal emulation and allows commodity WiFi devices to emulate custom wireless physical layer, including but not limited to, custom PHY-layer preambles and new ways of agile spectrum usage. It could also improve the performance of cross-technology communication and many other wireless applications by up to 50x, enabling high-speed data communication on par with commodity WiFi.

A new backscatter system of precise, adaptive and lightweight power adaptation towards energy-efficient backscatter readers. It learns the entangled correlation between backscatter channel conditions, transmit RF power and goodput by designing a modular neural network to avoid redundancy and any confusion in training. Experiment results show improvement in energy efficiency at readers by 3.5× and reduces power consumption by up to 80%.

FaceListener transforms the commodity headphone into an acoustic sensing device, which captures the face skin deformations caused by fa-cial muscle movements with different facial expressions. To ensure the recognition accuracy, FaceListener leverages the knowledge distillation technique to learn the subtle correlation between face skin deformation and the acoustic signal changes.

This is one of the few works that demonstrate critical security vulnerabilities of mainstream GPUs (QualComm Adreno GPU on Snapdragon SoCs) on smartphones, which allow an unprivileged attacker to eavesdrop the user’s sensitive credentials such as app username and password.