The development of neural networks has now reached the stage where
they are employed in a large variety of practical contexts.
However, to date the majority of such implementations have been in
software. While it is generally recognised that hardware
implementations could, through performance advantages, greatly
increase the use of neural networks, to date the relatively high
cost of developing Application-Specific Integrated Circuits (ASICs)
has meant that only a small number of hardware neurocomputers has
gone beyond the research-prototype stage. The situation has now
changed dramatically: with the appearance of large, dense, highly
parallel FPGA circuits it has now become possible to envisage
putting large-scale neural networks in hardware, to get high
performance at low costs. This in turn makes it practical to
develop hardware neural-computing devices for a wide range of
applications, ranging from embedded devices in high-volume/low-cost
consumer electronics to large-scale stand-alone neurocomputers. Not
surprisingly, therefore, research in the area has recently rapidly
increased, and even sharper growth can be expected in the next
decade or so.
Nevertheless, the many opportunities offered by FPGAs also come
with many challenges, since most of the existing body of knowledge
is based on ASICs (which are not as constrained as FPGAs). These
challenges range from the choice of data representation, to the
implementation of specialized functions, through to the realization
of massively parallel neural networks; and accompanying these are
important secondary issues, such as development tools and
technology transfer. All these issues are currently being
investigated by a large numberof researchers, who start from
different bases and proceed by different methods, in such a way
that there is no systematic core knowledge to start from, evaluate
alternatives, validate claims, and so forth. FPGA Implementations
of Neural Networks aims to be a timely one that fill this gap in
three ways: First, it will contain appropriate foundational
material and therefore be appropriate for advanced students or
researchers new to the field. Second, it will capture the state of
the art, in both depth and breadth and therefore be useful
researchers currently active in the field. Third, it will cover
directions for future research, i.e. embryonic areas as well as
more speculative ones.
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