11 papers
Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence
Chengwei Zhou, Zhaoyan Jia, Haotian Yu +6
Edge-based multimodal medical monitoring requires models that balance diagnostic accuracy with severe energy constraints. Continuous acquisition of ECG, PPG, EMG, and IMU streams r…
Light-Bound Transformers: Hardware-Anchored Robustness for Silicon-Photonic Computer Vision Systems
Xuming Chen, Deniz Najafi, Chengwei Zhou +6
Deploying Vision Transformers (ViTs) on near-sensor analog accelerators demands training pipelines that are explicitly aligned with device-level noise and energy constraints. We in…
Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification
Vikash Singh, Debargha Ganguly, Haotian Yu +5
Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagnostic impressions unsupported by…
LE-NeuS: Latency-Efficient Neuro-Symbolic Video Understanding via Adaptive Temporal Verification
Shawn Liang, Sahil Shah, Chengwei Zhou +5
Neuro-symbolic approaches to long-form video question answering (LVQA) have demonstrated significant accuracy improvements by grounding temporal reasoning in formal verification. H…
EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices
Arnab Sanyal, Gourav Datta, Prithwish Mukherjee +2
Large Language Models (LLMs) achieve strong performance across tasks, but face storage and compute challenges on edge devices. We propose EntroLLM, a compression framework combinin…
Rethinking Vision Transformer Depth via Structural Reparameterization
Chengwei Zhou, Vipin Chaudhary, Gourav Datta
The computational overhead of Vision Transformers in practice stems fundamentally from their deep architectures, yet existing acceleration strategies have primarily targeted algori…