  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-scatter
that encompasses all the key primitives of training embedding layers. When
prototyped on a real CPU-GPU system, Tensor Casting provides 1.9-21x
improvements in training throughput compared to state-of-the-art approaches.
