Neural networks in FPGAs

As FPGAs have increasingly become denser and faster, they are being utilized for many applications, including the implementation of neural networks. Ideally, FPGA implementations, being directly in hardware and having parallelism, will have performance advantages over software on conventional machines. But there is a great deal to be done to make the most of FPGAs and to prove their worth in implementing neural networks, especially in view of past failures in the implementation of neurocomputers. This paper looks at some of the relevant issues

Implementation of square-root and exponential functions for large FPGAs

Berlin, German

FPGA implementations of neural networks

Neural networks are employed in a large variety of practical contexts. However, the majority of such implementations have been in software. With the appearance of large and dense FPGA circuits, it has become possible to envisage putting large-scale neural networks in hardware. This title is intended for students or researchers of this field

Exploiting Java-ILP on a Simultaneous Multi-Trace

hariprakash.govindarajalu