PULP-NN: A Computing Library for Quantized Neural Network inference at the edge on RISC-V Based Parallel Ultra Low Power Clusters-Reference-Cited by-同舟云学术

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PULP-NN: A Computing Library for Quantized Neural Network inference at the edge on RISC-V Based Parallel Ultra Low Power Clusters

Published:2019-11 Issue: Volume: Page:
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Container-title:2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
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Author:

Garofalo Angelo,Rusci Manuele,Conti Francesco,Rossi Davide,Benini Luca

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8955687/8964633/08965067.pdf?arnumber=8965067

Cited by 25 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

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2. High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. NARS: Neuromorphic Acceleration through Register-Streaming Extensions on RISC-V Cores;Proceedings of the 21st ACM International Conference on Computing Frontiers: Workshops and Special Sessions;2024-05-07

4. Parallel GEMM-based convolution for deep learning on multicore RISC-V processors;The Journal of Supercomputing;2024-02-19

5. HAMSA-DI: A Low-Power Dual-Issue RISC-V Core Targeting Energy-Efficient Embedded Systems;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-01

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