SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference

1 October 2021

Papers citing "SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference"

7 / 7 papers shown

Title
Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs Aidan Ferguson Perry Gibson Lara DÁgata Parker McLeod Ferhat Yaman Amitabh Das Ian Colbert José Cano 58 0 0 12 Mar 2025
Accelerating PoT Quantization on Edge Devices Rappy Saha Jude Haris José Cano MQ 18 0 0 30 Sep 2024
Designing Efficient LLM Accelerators for Edge Devices Jude Haris Rappy Saha Wenhao Hu José Cano 24 7 0 01 Aug 2024
Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators Axel Stjerngren Perry Gibson José Cano 20 4 0 26 Apr 2022
Measuring the Algorithmic Efficiency of Neural Networks Danny Hernandez Tom B. Brown 235 94 0 08 May 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 950 20,561 0 17 Apr 2017
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights Aojun Zhou Anbang Yao Yiwen Guo Lin Xu Yurong Chen MQ 316 1,047 0 10 Feb 2017