  In recent years, domain-specific hardware has brought significant performance
improvements in deep learning (DL). Both industry and academia only focus on
throughput when evaluating these AI accelerators, which usually are custom
ASICs deployed in datacenter to speed up the inference phase of DL workloads.
Pursuing higher hardware throughput such as OPS (Operation Per Second) using
various optimizations seems to be their main design target. However, they
ignore the importance of accuracy in the DL nature. Motivated by this, this
paper argue that a single throughput metric can not comprehensively reflect the
real-world performance of AI accelerators. To reveal this pitfall, we evaluates
several frequently-used optimizations on a typical AI accelerator and
quantifies their impact on accuracy and throughout under representative DL
inference workloads. Based on our experimental results, we find that some
optimizations cause significant loss on accuracy in some workloads, although it
can improves the throughout. Furthermore, our results show the importance of
end-to-end evaluation in DL.
