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

Published: 17 October 2021 Publication History

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. RPAccel 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. Compared to previously proposed specialized recommendation accelerators and at iso-quality, we demonstrate that RPAccel improves latency and throughput by 3 × and 6 ×.

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cover image ACM Conferences
MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
October 2021
1322 pages
ISBN:9781450385572
DOI:10.1145/3466752
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  1. datacenter
  2. deep learning
  3. hardware accelerator
  4. personalized recommendation

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