Low Latency Implementations of CNN for Resource-Constrained IoT Devices
Author:
Affiliation:
1. Department of IT Convergence Engineering, Gachon University, Seongnam, South Korea
2. Department of Computer Engineering, Gachon University, Seongnam, South Korea
Funder
Institute of Information and Communications Technology Planning and Evaluation (IITP) under the High-Potential Individuals Global Training Program
National Research Foundation of Korea (NRF) funded by the Korean Government through Ministry of Science and ICT
Gachon University Research Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/8920/9969381/09881566.pdf?arnumber=9881566
Reference12 articles.
1. High-Performance Low-Memory Lowering: GEMM-based Algorithms for DNN Convolution
2. CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
3. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
4. Olympus: Reaching Memory-Optimality on DNN Processors
5. Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollers;rusci;arXiv 1905 13082,2019
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