Mix-GEMM: An efficient HW-SW Architecture for Mixed-Precision Quantized Deep Neural Networks Inference on Edge Devices-Reference-Cited by-同舟云学术

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Mix-GEMM: An efficient HW-SW Architecture for Mixed-Precision Quantized Deep Neural Networks Inference on Edge Devices

Published:2023-02 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
language:
Short-container-title:

Author:

Reggiani Enrico¹,Pappalardo Alessandro²,Doblas Max¹,Moreto Miquel¹,Olivieri Mauro³,Unsal Osman Sabri³,Cristal Adrián¹

Affiliation:

1. Polytechnic University of Catalonia

2. AMD AECG Research Labs,Dublin,Ireland

3. Barcelona Supercomputing Center

Funder

Generalitat de Catalunya

Ministry of Economy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10070856/10070923/10071076.pdf?arnumber=10071076

Reference80 articles.

1. CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices

2. Enhancing the Scalability of Multi-FPGA Stencil Computations via Highly Optimized HDL Components

3. DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs

4. Pytorch: An imperative style, high-performance deep learning library;paszke;Advances in Neural IInformation Processing Systems,2019

5. High performance convolutional neural networks for document processing;chellapilla;International Workshop on Frontiers in Handwriting Recognition,2006

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Benchmarking GPU Tensor Cores on General Matrix Multiplication Kernels through CUTLASS;Applied Sciences;2023-12-06

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