Deep Learning Accelerator on Programmable Heterogeneous System with RISC-V Processor

Published in 42rd International Convention on Information and Communication Technology, Electronics and Microelectronics, 2019

Available at: https://www.researchgate.net/profile/Branimir-Pervan/publication/335174529_Deep_Learning_Accelerator_on_Programmable_Heterogeneous_System_with_RISC-V_Processor/links/5d54d5a3299bf16f0738e252/Deep-Learning-Accelerator-on-Programmable-Heterogeneous-System-with-RISC-V-Processor.pdf

This paper explores a heterogeneous, open source, RISC-V based platform, called PULP (Parallel Ultra-Low-Power Processing Platform) with customizable MAC (multiply and accumulate) accelerator for neural network acceleration on edge computing devices with low power and performance-limited resources. To address the performance and power constraints we propose binarized implementation of a neural network accelerator. We show that binarized implementation is faster than the fixed-point accelerated implementation, albeit with some loss in precision, but still applicable to most of the use cases in edge computing.

@inproceedings{Strizic2019,
    author = {Strizic, Luka and Pervan, Branimir and Knezovic, Josip},
    booktitle = {2019 proceedings of the 42nd international convention MIPRO},
    pages = {1126--1131},
    publisher = {IEEE},
    title = ,
    url = {https://www.bib.irb.hr/1021661/download/1021661.16_cts_5493.pdf},
    year = {2019}
}