Dec 28, 2023 · We propose an embedding vector element quantization and compression method to reduce the memory footprint (capacity) required by the embedding tables.

We propose an embedding vector element quantization and compression method to reduce the memory footprint (capacity) required by the embedding tables.

Oct 22, 2024 · To resolve this problem, we propose an embedding vector element quantization and compression method to reduce the memory footprint (capacity) ...

Near-Memory Computing with Compressed Embedding Table for Personalized Recommendation. SessionNetworking Reception & Work-In-Progress Poster Session.

May 15, 2023 · Embedding compression techniques approach the problem to reduce the storage requirements of the embedding tables. The simplest such approach in ...

Specifically, it highlights the opportunity for the RecNMP architecture in which bandwidth-intensive embedding table operations are performed in the memory and.

[Dec. 2023] Our research paper "Near-Memory Computing with Compressed Embedding Table for Personalized Recommendation" is accepted to IEEE Transactions on ...

[TETC '24] Near-Memory Computing with Compressed Embedding Table for Personalized Recommendation Jeongmin Lim, Young Geun Kim, Sung Woo Chung, Farinaz ...

Both the filtering and ranking stage use embedding tables (ETs) to capture and store user behaviors and item characteristics. The large. ETs make the operations ...

The embedding table requires not only large memory capacity as the embedding table size for the recommendation system can reach several TBs [51,58] but also ...