76 citations · 76 across the 5 of their papers we have counts for
5 papers
TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI
Yuheng Zhang, Yizhao Wang, Da Zhu +19
We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-context and latency-sensitive p…
TuringViT: Making SOTA Vision Transformers Accessible to All
Qiman Wu, Hanlin Chen, Lyujie Chen +19
Modern VLMs and VLA systems commonly adopt off-the-shelf ViTs such as SigLIP2 as visual encoders, but diverse downstream requirements in latency, temporal modeling, and VLM integra…
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Jian Wang, Chenhui Gou, Qiman Wu +4
Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in th…
PalQuant: Accelerating High-precision Networks on Low-precision Accelerators
Qinghao Hu, Gang Li, Qiman Wu +1
Recently low-precision deep learning accelerators (DLAs) have become popular due to their advantages in chip area and energy consumption, yet the low-precision quantized models on…
MixFormer: Mixing Features across Windows and Dimensions
Qiang Chen, Qiman Wu, Jian Wang +5
While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs…