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
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_Report_U1722196K.pdf
  Restricted Access
5.89 MBAdobe PDFView/Open

Page view(s)

143
Updated on Jun 25, 2022

Download(s)

13
Updated on Jun 25, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.