Memory controller

Operating system

Computer architecture

Distributed computing

Artificial intelligence

Parallel computing

Memory bandwidth

CAS latency

Inference

Computer science

Embedding

Scalability

Speedup

Dram

Computer hardware

Semiconductor memory

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 cooptimization techniques such as memory-side caching, tableaware 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.

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing