RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
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 …