  Deep Neural Networks (DNNs) are witnessing increased adoption in multiple
domains owing to their high accuracy in solving real-world problems. However,
this high accuracy has been achieved by building deeper networks, posing a
fundamental challenge to the low latency inference desired by user-facing
applications. Current low latency solutions trade-off on accuracy or fail to
exploit the inherent temporal locality in prediction serving workloads.
  We observe that caching hidden layer outputs of the DNN can introduce a form
of late-binding where inference requests only consume the amount of computation
needed. This enables a mechanism for achieving low latencies, coupled with an
ability to exploit temporal locality. However, traditional caching approaches
incur high memory overheads and lookup latencies, leading us to design learned
caches - caches that consist of simple ML models that are continuously updated.
We present the design of GATI, an end-to-end prediction serving system that
incorporates learned caches for low-latency DNN inference. Results show that
GATI can reduce inference latency by up to 7.69X on realistic workloads.
