scholarly journals The Coarse-Grained/Fine-Grained Logic Interface in FPGAs with Embedded Floating-Point Arithmetic Units

2008 ◽  
Vol 2008 ◽  
pp. 1-10
Author(s):  
Chi Wai Yu ◽  
Julien Lamoureux ◽  
Steven J. E. Wilton ◽  
Philip H. W. Leong ◽  
Wayne Luk

This paper examines the interface between fine-grained and coarse-grained programmable logic in FPGAs. Specifically, it presents an empirical study that covers the location, pin arrangement, and interconnect between embedded floating point units (FPUs) and the fine-grained logic fabric in FPGAs. It also studies this interface in FPGAs which contain both FPUs and embedded memories. The results show that (1) FPUs should have a square aspect ratio; (2) they should be positioned near the center of the FPGA; (3) their I/O pins should be arranged around all four sides of the FPU; (4) embedded memory should be located between the FPUs; and (5) connecting higher I/O density coarse-grained blocks increases the demand for routing resources. The hybrid FPGAs with embedded memory required 12% wider channels than the case where embedded memory is not used.

Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham

A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.


2020 ◽  
Vol 39 (6) ◽  
pp. 1-16
Author(s):  
Gianmarco Cherchi ◽  
Marco Livesu ◽  
Riccardo Scateni ◽  
Marco Attene

1964 ◽  
Vol 7 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Robert T. Gregory ◽  
James L. Raney

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