RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance

TL;DR

RecPipe introduces a co-designed system and hardware accelerator that jointly optimize recommendation quality and performance, achieving significant improvements in latency and throughput for deep learning recommendation systems.

Contribution

The paper presents RecPipe, a novel multi-stage pipeline decomposition and hardware accelerator design that jointly optimize recommendation quality and system efficiency.

Findings

RPAccel improves latency by 3x and throughput by 6x at iso-quality.

RecPipe's multi-stage pipeline enables better parallelism and efficiency.

Hardware-aware scheduling enhances ranking performance on commodity platforms.

Abstract

Deep learning recommendation systems must provide high quality, personalized content under strict tail-latency targets and high system loads. This paper presents RecPipe, a system to jointly optimize recommendation quality and inference performance. Central to RecPipe is decomposing recommendation models into multi-stage pipelines to maintain quality while reducing compute complexity and exposing distinct parallelism opportunities. RecPipe implements an inference scheduler to map multi-stage recommendation engines onto commodity, heterogeneous platforms (e.g., CPUs, GPUs).While the hardware-aware scheduling improves ranking efficiency, the commodity platforms suffer from many limitations requiring specialized hardware. Thus, we design RecPipeAccel (RPAccel), a custom accelerator that jointly optimizes quality, tail-latency, and system throughput. RPAc-cel is designed specifically to exploit the distinct design space opened via RecPipe. In particular, RPAccel processes queries in sub-batches to pipeline recommendation stages, implements dual static and dynamic embedding caches, a set of top-k filtering units, and a reconfigurable systolic array. Com-pared to prior-art and at iso-quality, we demonstrate that RPAccel improves latency and throughput by 3x and 6x.