  We develop an edge-assisted object recognition system with the aim of
studying the system-level trade-offs between end-to-end latency and object
recognition accuracy. We focus on developing techniques that optimize the
transmission delay of the system and demonstrate the effect of image encoding
rate and neural network size on these two performance metrics. We explore
optimal trade-offs between these metrics by measuring the performance of our
real time object recognition application. Our measurements reveal hitherto
unknown parameter effects and sharp trade-offs, hence paving the road for
optimizing this key service. Finally, we formulate two optimization problems
using our measurement-based models and following a Pareto analysis we find that
careful tuning of the system operation yields at least 33% better performance
for real time conditions, over the standard transmission method.
