Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Abstract and Introduction | HackerNoonApparate effectively reduces latency in ML inference by using early exits without compromising throughput or accuracy.
Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Comparisons | HackerNoonApparate maintains accuracy better than existing early-exit models, achieving lower latency while adhering to tight accuracy constraints.
Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Abstract and Introduction | HackerNoonApparate effectively reduces latency in ML inference by using early exits without compromising throughput or accuracy.
Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Comparisons | HackerNoonApparate maintains accuracy better than existing early-exit models, achieving lower latency while adhering to tight accuracy constraints.
Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Evaluation and Methodology | HackerNoonApparate improves latency in NLP and CV workloads while maintaining accuracy, offering advantages over traditional early-exit models.