RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

International Symposium on Computer Architecture (ISCA)


Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose a fundamental challenge to accelerate. This paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference. The in-depth characterization of production-grade recommendation models shows that embedding operations with high model-, operator- and data-level parallelism lead to memory bandwidth saturation, limiting recommendation inference performance. We propose RecNMP which provides a scalable solution to improve system throughput, supporting a broad range of sparse embedding models. RecNMP is specifically tailored to production environments with heavy co-location of operators on a single server. Several hardware/software co-optimization techniques such as memory-side caching, table-aware packet scheduling, and hot entry profiling are studied, providing up to 9.8× memory latency speedup over a highly optimized baseline. Overall, RecNMP offers 4.2× throughput improvement and 45.8% memory energy savings.

Related Publications

All Publications

11-Gbps Broadband Modem-Agnostic Line-of-Sight MIMO Over the Range of 13 km

Yan Yan, Pratheep Bondalapati, Abhishek Tiwari, Chiyun Xia, Andy Cashion, Dawei Zhang, Tobias Tiecke, Qi Tang, Michael Reed, Dudi Shmueli, Hongyu Zhou, Bob Proctor, Joseph Stewart

IEEE GLOBECOM - January 21, 2019

Weak-Attention Suppression For Transformer Based Speech Recognition

Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer

Interspeech - October 26, 2020

Machine Learning in Compilers: Past, Present, and Future

Hugh Leather, Chris Cummins

FDL - September 14, 2020

Unsupervised Cross-Domain Singing Voice Conversion

Adam Polyak, Lior Wolf, Yossi Adi, Yaniv Taigman

Interspeech - August 8, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy