150 citations · 200 across the 16 of their papers we have counts for
8 papers · 1 filter
Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy
Yu Feng, Weikai Lin, Yuge Cheng +6
3D Gaussian Splatting (3DGS) has vastly advanced the pace of neural rendering, but it remains computationally demanding on today's mobile SoCs. To address this challenge, we propos…
StreamGrid: Streaming Point Cloud Analytics via Compulsory Splitting and Deterministic Termination
Yu Feng, Zheng Liu, Weikai Lin +6
Point clouds are increasingly important in intelligent applications, but frequent off-chip memory traffic in accelerators causes pipeline stalls and leads to high energy consumptio…
Potamoi: Accelerating Neural Rendering via a Unified Streaming Architecture
Yu Feng, Weikai Lin, Zihan Liu +6
Neural Radiance Field (NeRF) has emerged as a promising alternative for photorealistic rendering. Despite recent algorithmic advancements, achieving real-time performance on today'…
Cicero: Addressing Algorithmic and Architectural Bottlenecks in Neural Rendering by Radiance Warping and Memory Optimizations
Yu Feng, Zihan Liu, Jingwen Leng +2
Neural Radiance Field (NeRF) is widely seen as an alternative to traditional physically-based rendering. However, NeRF has not yet seen its adoption in resource-limited mobile syst…
OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
Cong Guo, Jiaming Tang, Weiming Hu +6
Transformer-based large language models (LLMs) have achieved great success with the growing model size. LLMs' size grows by every two years, which outpaces the hardware…
ImaGen: A General Framework for Generating Memory- and Power-Efficient Image Processing Accelerators
Nisarg Ujjainkar, Jingwen Leng, Yuhao Zhu
Image processing algorithms are prime targets for hardware acceleration as they are commonly used in resource- and power-limited applications. Today's image processing accelerator…
ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
Zhihui Zhang, Jingwen Leng, Shuwen Lu +5
Graph neural networks (GNNs) start to gain momentum after showing significant performance improvement in a variety of domains including molecular science, recommendation, and trans…
Ptolemy: Architecture Support for Robust Deep Learning
Yiming Gan, Yuxian Qiu, Jingwen Leng +2
Deep learning is vulnerable to adversarial attacks, where carefully-crafted input perturbations could mislead a well-trained Deep Neural Network to produce incorrect results. Today…