Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/149624
Title: | Implementation and performance evaluation of NVDLA based deep learning accelerator hardware | Authors: | Song, Tin Chen | Keywords: | Engineering::Electrical and electronic engineering::Microelectronics Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
Issue Date: | 2021 | Publisher: | Nanyang Technological University | Source: | Song, T. C. (2021). Implementation and performance evaluation of NVDLA based deep learning accelerator hardware. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149624 | Project: | B3035-201 | Abstract: | This report shows the steps needed for one to implement a deep Learning hardware accelerator based on NVIDIA DL accelerator NVDLA architecture on high-performance emulation. The simulation platform chosen was Firesim. NVDLA architecture is an industrial-grade opensource hardware accelerator project for Deep Learning inference acceleration. NVDLA not only provides full hardware design source file but also provides the corresponding software library to directly deploy DL networks that are trained and optimized using NVIDIA GPU based AI system. On top of the implemented NVDLA accelerator, evaluation on the performance of the hardware accelerator was done based on existing research papers. | URI: | https://hdl.handle.net/10356/149624 | Schools: | School of Electrical and Electronic Engineering | Research Centres: | NVIDIA AI Technology Centre | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Student Reports (FYP/IA/PA/PI) |
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File | Description | Size | Format | |
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FYP_Report_U1722196K.pdf Restricted Access | 5.89 MB | Adobe PDF | View/Open |
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