Proceedings Article10.1109/ICETET.2015.37
Configuration Memory Based Dynamic Coarse Grained Reconfigurable Multicore Architecture for 8 Point FFT
Rahul K. Hiware,Dinesh Padole +1 more
- 01 Nov 2015
- pp 19-22
3
TL;DR: In this article, the authors proposed and configuration memory based Coarse-grained architecture (CGRA) system which can change the hardware Configuration dynamically according to applications, and the necessary Data flow diagram/graph (DFG) also presented and implemented.
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Abstract: Hardware reconfigure model provides flexibility to same hardware for different application. Coarse-grained Architecture (CGRA) is best example of the reconfigurable system. This paper proposed and configuration memory based CGRA system which can change the hardware Configuration dynamically according to applications. Configuration memory will consist of multiple such DFGs that will configure the hardware according to the detected application. Author has proposed CGRA system with 4X4 Processing Elements (PE) and configured for 8 point FFT application. The necessary Data flow diagram/graph (DFG) also presented and implemented.
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Citations
Bind the gap: compiling real software to hardware FFT accelerators
Jackson Woodruff,Jordi Armengol-Estapé,Sam Ainsworth,Michael O'Boyle +3 more
- 09 Jun 2022
TL;DR: FACC (Fourier ACcelerator Compiler), a novel approach to automatically map legacy code to Fourier Transform accelerators, which automatically generates drop-in replacement adapters using Input-Output (IO)-based program synthesis that bridge the gap between user code and accelerators.
Design of Binary Neural Network Soft System for Pattern Detection using HDL Tool
Pankaj H. Chandankhede,Abhijit S. Titarmare +1 more
- 23 Feb 2023
TL;DR: In this paper , the effect of different FPGA performances in Convolution Neural Network (CNN) processer has been analyzed in a real-time application using real-world images.
Design of Binary Neural Network Soft System for Pattern Detection using HDL Tool
23 Feb 2023
TL;DR: In this paper , the effect of different FPGA performances in Convolution Neural Network (CNN) processer is discussed. But, the authors did not consider to perform or to process the imagery information because FPGAs have the capability of parallel process or concurrent processing hardware facility.
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