150 citations · 200 across the 16 of their papers we have counts for
16 papers
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…
JUNO: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping
Zihan Liu, Wentao Ni, Jingwen Leng +6
Approximate nearest neighbor (ANN) search is a widely applied technique in modern intelligent applications, such as recommendation systems and vector databases. Therefore, efficien…
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…
ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization
Cong Guo, Chen Zhang, Jingwen Leng +5
Quantization is a technique to reduce the computation and memory cost of DNN models, which are getting increasingly large. Existing quantization solutions use fixed-point integer o…
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Cong Guo, Yuxian Qiu, Jingwen Leng +6
Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the o…
Block-Skim: Efficient Question Answering for Transformer
Yue Guan, Zhengyi Li, Jingwen Leng +3
Transformer models have achieved promising results on natural language processing (NLP) tasks including extractive question answering (QA). Common Transformer encoders used in NLP…
Characterizing and Demystifying the Implicit Convolution Algorithm on Commercial Matrix-Multiplication Accelerators
Yangjie Zhou, Mengtian Yang, Cong Guo +5
Many of today's deep neural network accelerators, e.g., Google's TPU and NVIDIA's tensor core, are built around accelerating the general matrix multiplication (i.e., GEMM). However…
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…
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity
Cong Guo, Bo Yang Hsueh, Jingwen Leng +7
Network pruning can reduce the high computation cost of deep neural network (DNN) models. However, to maintain their accuracies, sparse models often carry randomly-distributed weig…
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…
Balancing Efficiency and Flexibility for DNN Acceleration via Temporal GPU-Systolic Array Integration
Cong Guo, Yangjie Zhou, Jingwen Leng +6
The research interest in specialized hardware accelerators for deep neural networks (DNN) spikes recently owing to their superior performance and efficiency. However, today's DNN a…
Adversarial Defense Through Network Profiling Based Path Extraction
Yuxian Qiu, Jingwen Leng, Cong Guo +4
Recently, researchers have started decomposing deep neural network models according to their semantics or functions. Recent work has shown the effectiveness of decomposed functiona…