English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Graph Neural Network (GNN) is a variant of Deep Neural Networks (DNNs)operating on graphs. However, GNNs are more complex compared to traditionalDNNs as they simultaneously exhibit features of both DNN and graphapplications. As a result, architectures specifically optimized for either DNNsor graph applications are not suited for GNN training. In this work, we proposea 3D heterogeneous manycore architecture for on-chip GNN training to addressthis problem. The proposed architecture, ReGraphX, involves heterogeneous ReRAMcrossbars to fulfill the disparate requirements of both DNN and graphcomputations simultaneously. The ReRAM-based architecture is complemented witha multicast-enabled 3D NoC to improve the overall achievable performance. Wedemonstrate that ReGraphX outperforms conventional GPUs by up to 3.5X (on anaverage 3X) in terms of execution time, while reducing energy consumption by asmuch as 11X.

ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks