%0 Journal Article
	%A Thomas Behan and  Zaiyi Liao and  Lian Zhao and  Chunting Yang
	%D 2008
	%J International Journal of Electrical and Computer Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 24, 2008
	%T Accelerating Integer Neural Networks On Low Cost DSPs
	%U https://publications.waset.org/pdf/2049
	%V 24
	%X In this paper, low end Digital Signal Processors (DSPs)
are applied to accelerate integer neural networks. The use of DSPs
to accelerate neural networks has been a topic of study for some
time, and has demonstrated significant performance improvements.
Recently, work has been done on integer only neural networks, which
greatly reduces hardware requirements, and thus allows for cheaper
hardware implementation. DSPs with Arithmetic Logic Units (ALUs)
that support floating or fixed point arithmetic are generally more
expensive than their integer only counterparts due to increased circuit
complexity. However if the need for floating or fixed point math
operation can be removed, then simpler, lower cost DSPs can be
used. To achieve this, an integer only neural network is created in
this paper, which is then accelerated by using DSP instructions to
improve performance.
	%P 2711 - 2714