Graphicionado: A high-performance and energy-efficient accelerator for graph analytics

By Tae Jun Ham, Lisa Wu, Narayanan Sundaram, Nadathur Rajagopalan Satish, Margaret Martonosi. Micro, 2016

351 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/23e661acc510afd4398b7d21bd63ea5e89bc8d45

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shuai Che
2. Yuan Xie
3. Lei Deng
4. Xiaochun Ye
5. Mingyu Yan
6. Ling Liang
7. Zhimin Zhang
8. Yujing Feng

In Computer Vision and Pattern Recognition

1. Shuai Che
2. Yuan Xie
3. Lei Deng
4. Xiaochun Ye
5. Runbin Shi
6. Dongrui Fan

In Information Systems

1. Steven K. Reinhardt

Citations in the corpus

• AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing (2019, background): “Ham et al. [59] propose Graphicionado, a vertex-centric acceleration framework for graph analytics applications.”
• HyGCN: A GCN Accelerator with Hybrid Architecture (2020, background): “For example, Graphicionado [17] is tailored for graph analty-ics; while TPU [22] focuses on the acceleration of neural networks.”