  In this paper, we provide a deep dive into the deployment of inference
accelerators at Facebook. Many of our ML workloads have unique characteristics,
such as sparse memory accesses, large model sizes, as well as high compute,
memory and network bandwidth requirements. We co-designed a high-performance,
energy-efficient inference accelerator platform based on these requirements. We
describe the inference accelerator platform ecosystem we developed and deployed
at Facebook: both hardware, through Open Compute Platform (OCP), and software
framework and tooling, through Pytorch/Caffe2/Glow. A characteristic of this
ecosystem from the start is its openness to enable a variety of AI accelerators
from different vendors. This platform, with six low-power accelerator cards
alongside a single-socket host CPU, allows us to serve models of high
complexity that cannot be easily or efficiently run on CPUs. We describe
various performance optimizations, at both platform and accelerator level,
which enables this platform to serve production traffic at Facebook. We also
share deployment challenges, lessons learned during performance optimization,
as well as provide guidance for future inference hardware co-design.
