SmartVL jointly adapts both input visual tokens and LLM compute for each sample, balancing vision-side efficiency and reasoning-side depth under a global budget. A token controller selects informative visual tokens while a compute controller adjusts LLM execution, and both are trained to cooperate through a shared budget signal. This coordination yields a better accuracy-efficiency trade-off on MLLM benchmarks than optimizing token pruning or compute skipping in isolation.
@inproceedings{wang2026smartvl,title={Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs},author={Wang, Pengcheng and Wang, Zhiquan and Lee, Jayoung and Xu, Zhuoyan and Xu, Ran and Bagchi, Saurabh and Li, Yin and Chaterji, Somali},booktitle={Proceedings of the European Conference on Computer Vision},year={2026},}
MobiSys
Agile3D: Adaptive Contention- and Content-Aware 3D Object Detection for Embedded GPUs
Agile3D is an adaptive 3D object detection system for embedded GPUs. It integrates a cross-model multi-branch execution framework with a contention- and content-aware reinforcement learning controller to dynamically balance detection accuracy and inference latency under changing input scenes and hardware contention.
@inproceedings{wang2025agile3d,title={Agile3D: Adaptive Contention- and Content-Aware 3D Object Detection for Embedded GPUs},author={Wang, Pengcheng and Liu, Zhuoming and Bagchi, Shayok and Xu, Ran and Bagchi, Saurabh and Li, Yin and Chaterji, Somali},booktitle={Proceedings of the 23rd ACM International Conference on Mobile Systems, Applications, and Services},year={2025},publisher={ACM},}
TODAES
Virtuoso: Energy- and Latency-Aware Streamlining of Streaming Videos on SOCs
Virtuoso is an efficient and adaptive video object detection system that jointly optimizes accuracy, energy efficiency, and latency on system-on-chips. It uses a multi-branch execution kernel and a lightweight runtime scheduler to select operating points across the accuracy-energy-latency space.
@article{lee2023virtuoso,title={Virtuoso: Energy- and Latency-Aware Streamlining of Streaming Videos on SOCs},author={Lee, Jayoung and Wang, Pengcheng and Xu, Ran and Jain, Sarthak and Dasari, Venkat and Weston, Noah and Li, Yin and Bagchi, Saurabh and Chaterji, Somali},journal={ACM Transactions on Design Automation of Electronic Systems},year={2023},publisher={ACM},doi={10.1145/3564289},}
EuroSys
LiteReconfig: Cost and Content Aware Reconfiguration of Video Object Detection Systems for Mobile GPUs
LiteReconfig is an efficient adaptive video object detection framework for mobile GPUs. It uses a cost-benefit analyzer and a content-aware accuracy prediction model to decide which features to evaluate and which execution branch to run at inference time.
@inproceedings{xu2022litereconfig,title={LiteReconfig: Cost and Content Aware Reconfiguration of Video Object Detection Systems for Mobile GPUs},author={Xu, Ran and Lee, Jayoung and Wang, Pengcheng and Bagchi, Saurabh and Li, Yin and Chaterji, Somali},booktitle={Proceedings of the European Conference on Computer Systems},year={2022},publisher={ACM},doi={10.1145/3492321.3519577},}
TOSN
ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients
ApproxNet is a video object classification system for embedded and mobile clients. It enables runtime approximation within a single neural network to meet latency and accuracy trade-offs under changing content, resource contention, and user requirements.
@article{xu2022approxnet,title={ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients},author={Xu, Ran and Kumar, Rakesh and Wang, Pengcheng and Bai, Peter and Meghanath, Ganga and Chaterji, Somali and Mitra, Subrata and Bagchi, Saurabh},journal={ACM Transactions on Sensor Networks},year={2022},publisher={ACM},doi={10.1145/3463530},}
SenSys
ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles
ApproxDet is an adaptive video object detection framework for mobile devices. It dynamically selects execution branches to meet accuracy-latency objectives under changing video content, compute contention, and user requirements.
@inproceedings{xu2020approxdet,title={ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles},author={Xu, Ran and Zhang, Chen-lin and Wang, Pengcheng and Lee, Jayoung and Mitra, Subrata and Chaterji, Somali and Li, Yin and Bagchi, Saurabh},booktitle={Proceedings of the ACM Conference on Embedded Networked Sensor Systems},year={2020},publisher={ACM},doi={10.1145/3384419.3431159},}
