activity
20202022
most citedArchitectural Implications of Graph Neural Networks

36 citations · 46 across the 7 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.DC2020★ 1 cited

DLFusion: An Auto-Tuning Compiler for Layer Fusion on Deep Neural Network Accelerator

Zihan Liu, Jingwen Leng, Quan Chen +4

Many hardware vendors have introduced specialized deep neural networks (DNN) accelerators owing to their superior performance and efficiency. As such, how to generate and optimize…

cs.CL2020★ 2 cited

How Far Does BERT Look At:Distance-based Clustering and Analysis of BERTs Attention

Yue Guan, Jingwen Leng, Chao Li +2

Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mec…

cs.AR2020★ 36 cited

Architectural Implications of Graph Neural Networks

Zhihui Zhang, Jingwen Leng, Lingxiao Ma +3

Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achie…

cs.DC2020★ 1 cited

Towards QoS-Aware and Resource-Efficient GPU Microservices Based on Spatial Multitasking GPUs In Datacenters

Wei Zhang, Quan Chen, Kaihua Fu +6

While prior researches focus on CPU-based microservices, they are not applicable for GPU-based microservices due to the different contention patterns. It is challenging to optimize…

cs.DC2020

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…