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. 1912.09356
  4. Cited By
FQ-Conv: Fully Quantized Convolution for Efficient and Accurate
  Inference

FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference

19 December 2019
Bram-Ernst Verhoef
Nathan Laubeuf
S. Cosemans
P. Debacker
Ioannis A. Papistas
A. Mallik
D. Verkest
    MQ
ArXivPDFHTML

Papers citing "FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference"

3 / 3 papers shown
Title
Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time
Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time
Matteo Risso
Alessio Burrello
Daniele Jahier Pagliari
51
0
0
24 Feb 2025
Precision-aware Latency and Energy Balancing on Multi-Accelerator
  Platforms for DNN Inference
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference
Matteo Risso
Alessio Burrello
G. M. Sarda
Luca Benini
Enrico Macii
M. Poncino
Marian Verhelst
Daniele Jahier Pagliari
28
4
0
08 Jun 2023
MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated
  Edge Inference
MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference
J. Farley
A. Gerstlauer
FedML
28
5
0
14 Jul 2021
1