Separable 2D Convolution with Polymorphic Register Files
Paper in proceeding, 2013
This paper studies the performance of separable 2D convolution on multi-lane Polymorphic Register Files (PRFs). We present a matrix transposition algorithm optimized for PRFs, and a 2D vectorized convolution algorithm which avoids strided memory accesses. We compare the throughput of our PRF to the NVIDIA Tesla C2050 GPU. The results show that even in bandwidth constrained systems, multi-lane PRFs can outperform the GPU for 9 × 9 or larger mask sizes.
Author
Catalin Ciobanu
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
Georgi Gaydadjiev
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
03029743 (ISSN) 16113349 (eISSN)
Vol. 7767 LNCS 317-328978-3-642-36423-5 (ISBN)
Subject Categories
Computer Engineering
Embedded Systems
Computer Systems
Areas of Advance
Information and Communication Technology
DOI
10.1007/978-3-642-36424-2_27
ISBN
978-3-642-36423-5