Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Evaluation and Methodology | HackerNoon
Briefly

from Hackernoon4 months ago
Apparate significantly reduces latency across various workloads, outperforming traditional early-exit models without sacrificing accuracy or tail-latency constraints, making it a versatile solution.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-evaluation-and-methodology
Our evaluation shows Apparate can achieve a 40.5-91.5% reduction in 25th percentile and median latencies for computer vision tasks, indicating substantial efficiency improvements.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-evaluation-and-methodology
While existing early-exit models can jeopardize accuracy by up to 23.9%, Apparate consistently meets both accuracy and tail-latency specifications across diverse configurations.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-evaluation-and-methodology
One notable benefit of Apparate is its capability to adapt to different model architectures and configurations fluently, maintaining performance under varying constraints.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-evaluation-and-methodology
Read at Hackernoon
#apparate #nlp #computer-vision #early-exit-models #latency-optimization
[
|
]