AI in EE

AI IN DIVISIONS

AI in Computer Division

AI in EE

AI IN DIVISIONS

AI in Computer Division ​

AI in Computer Division

Youngeun Kwon, Yunjae Lee, and Minsoo Rhu, "Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training," The 27th IEEE International Symposium on High-Performance Computer Architecture (HPCA-27), Seoul, South Korea, Feb.

Abstract

Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters. As such, architectural solutions for high-performance recommendation inference have recently been the target of several prior literatures. Unfortunately, little have been explored and understood regarding the training side of this emerging ML workload. In this paper, we first perform a detailed workload characterization study on training recommendations, root-causing sparse embedding layer training as one of the most significant performance bottlenecks. We then propose our algorithm-architecture co-design called Tensor Casting, which enables the development of a generic accelerator architecture for tensor gather-reduce that encompasses all the key primitives of training embedding layers. When prototyped on a real CPU-GPU system, Tensor Casting provides 1.9-15x improvements in training throughput compared to state-of-the-art approaches.