  Web applications have increasingly adopted Deep Learning (DL) through
in-browser inference, wherein DL inference performs directly within Web
browsers. The actual performance of in-browser inference and its impacts on the
quality of experience (QoE) remain unexplored, and urgently require new QoE
measurements beyond traditional ones, e.g., mainly focusing on page load time.
To bridge this gap, we make the first comprehensive performance measurement of
in-browser inference to date. Our approach proposes new metrics to measure
in-browser inference: responsiveness, smoothness, and inference accuracy. Our
extensive analysis involves 9 representative DL models across Web browsers of
50 popular PC devices and 20 mobile devices. The results reveal that in-browser
inference exhibits a substantial latency gap, averaging 16.9 times slower on
CPU and 4.9 times slower on GPU compared to native inference on PC devices. The
gap on mobile CPU and mobile GPU is 15.8 times and 7.8 times, respectively.
Furthermore, we identify contributing factors to such latency gap, including
underutilized hardware instruction sets, inherent overhead in the runtime
environment, resource contention within the browser, and inefficiencies in
software libraries and GPU abstractions. Additionally, in-browser inference
imposes significant memory demands, at times exceeding 334.6 times the size of
the DL models themselves, partly attributable to suboptimal memory management.
We also observe that in-browser inference leads to a significant 67.2% increase
in the time it takes for GUI components to render within Web browsers,
significantly affecting the overall user QoE of Web applications reliant on
this technology
