ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1906.11307
  4. Cited By
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency
  Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers

One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers

26 June 2019
Matthew Halpern
Behzad Boroujerdian
Todd W. Mummert
Evelyn Duesterwald
Vijay Janapa Reddi
ArXiv (abs)PDFHTML

Papers citing "One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers"

1 / 1 papers shown
Title
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Arya Tschand
Arun Tejusve Raghunath Rajan
S. Idgunji
Anirban Ghosh
J. Holleman
...
Rowan Taubitz
Sean Zhan
Scott Wasson
David Kanter
Vijay Janapa Reddi
130
3
0
15 Oct 2024
1