  While Deep Learning (DL) technologies are a promising tool to solve
networking problems that map to classification tasks, their computational
complexity is still too high with respect to real-time traffic measurements
requirements. To reduce the DL inference cost, we propose a novel caching
paradigm, that we named approximate-key caching, which returns approximate
results for lookups of selected input based on cached DL inference results.
While approximate cache hits alleviate DL inference workload and increase the
system throughput, they however introduce an approximation error. As such, we
couple approximate-key caching with an error-correction principled algorithm,
that we named auto-refresh. We analytically model our caching system
performance for classic LRU and ideal caches, we perform a trace-driven
evaluation of the expected performance, and we compare the benefits of our
proposed approach with the state-of-the-art similarity caching -- testifying
the practical interest of our proposal.
