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

Recent studies from several hyperscalars pinpoint to embedding layers as themost memory-intensive deep learning (DL) algorithm being deployed in today'sdatacenters. This paper addresses the memory capacity and bandwidth challengesof embedding layers and the associated tensor operations. We present ourvertically integrated hardware/software co-design, which includes a custom DIMMmodule enhanced with near-data processing cores tailored for DL tensoroperations. These custom DIMMs are populated inside a GPU-centric systeminterconnect as a remote memory pool, allowing GPUs to utilize for scalablememory bandwidth and capacity expansion. A prototype implementation of ourproposal on real DL systems shows an average 6.2-17.6x performance improvementon state-of-the-art recommender systems.

TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning