Student collaborators I have had the privilege of working with.
Research is team work. Along the way I have worked closely with — and learned from — excellent MS and BS students at Northeastern. This page gives them credit.
Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, current VLA models suffer from two drawbacks: (i) generation of massive tokens leading to high inference latency and increased training cost, and (ii) insufficient utilization of generated actions resulting in potential performance loss. To address these issues, we develop a training framework to finetune VLA models for generating significantly fewer action tokens with high parallelism, effectively reducing inference latency and training cost. Furthermore, we introduce an inference optimization technique with a novel voting-based ensemble strategy to combine current and previous action predictions, improving the utilization of generated actions and overall performance. Our results demonstrate that we achieve superior performance compared with state-of-the-art VLA models, achieving significantly higher success rates and 39x faster inference than OpenVLA with 46 Hz throughput on edge platforms, demonstrating practical deployability.
@inproceedings{lin2025vote,title={Vision-Language-Action Model Optimization for Real-Time Robotic Manipulation},author={Lin, Juyi and Taherin, Amir and Akbari, Arash and Akbari, Arman and Lu, Lei and Chen, Guangyu and Padir, Taskin and Yang, Xiaomeng and Srinivasa, Jayanth and Lin, Xue and Kaeli, David and Wang, Yanzhi and Zhao, Pu and Shen, Xuan},booktitle={Artificial Neural Networks and Machine Learning -- ICANN 2026 (Lecture Notes in Computer Science, Springer)},year={2026},pages={279--291},doi={10.1007/978-3-032-38404-1_23},}
Distinguished Artifact Award at IISWC 2026, recognizing the open-source Hydra framework and its released 107K-record per-prompt telemetry corpus.
Edge LLM deployment is shaped by more than model size and precision: inference backend, hardware platform, memory traffic, and power management all affect latency and efficiency. We present Hydra, a common-schema, phase-aware workload characterization framework for LLM inference on edge SoCs. Hydra instruments HuggingFace Transformers and llama.cpp with a shared per-prompt timing schema and fuses those records with hardware telemetry, enabling a multi-dimensional characterization of performance, system-resource utilization, and efficiency across prefill and decode phases. Using Hydra, we evaluate three consecutive edge System-on-Chip (SoC) generations (AGX Xavier, AGX Orin, and AGX Thor), 13 instruction-tuned LLMs from seven families, five execution formats, and consider input/output-length sensitivity. The resulting artifact contains roughly 107K per-prompt records and will be released with Hydra. Our analysis shows that aggregate latency alone hides key deployment effects: backend structure changes where latency is introduced, quantization reduces memory traffic and energy but does not predict power monotonically, and SoC generation changes how utilization and efficiency should be interpreted. By connecting phase-level timing with system-resource utilization and efficiency metrics, Hydra enables reproducible, phase-aware characterization of edge LLM inference.
@inproceedings{taherin2026hydra,title={Hydra: Phase-Aware Workload Characterization of LLM Inference across Edge SoC Generations, Backends, and Quantization Levels},author={Taherin, Amir and Taghipour Anvari, Sana and Amante, Charles and Chen, Yixiao and Noroian, Ruben and Feric, Zlatan and Bohm Agostini, Nicolas and Zhao, Pu and Cano, José and Ren, Bin and Wang, Yanzhi and Kaeli, David},booktitle={IEEE International Symposium on Workload Characterization (IISWC)},year={2026},pages={471--485},doi={10.1109/IISWC71020.2026.00048},}