59,116 research outputs found
FPGA-Based Bandwidth Selection for Kernel Density Estimation Using High Level Synthesis Approach
FPGA technology can offer significantly hi\-gher performance at much lower
power consumption than is available from CPUs and GPUs in many computational
problems. Unfortunately, programming for FPGA (using ha\-rdware description
languages, HDL) is a difficult and not-trivial task and is not intuitive for
C/C++/Java programmers. To bring the gap between programming effectiveness and
difficulty the High Level Synthesis (HLS) approach is promoting by main FPGA
vendors. Nowadays, time-intensive calculations are mainly performed on GPU/CPU
architectures, but can also be successfully performed using HLS approach. In
the paper we implement a bandwidth selection algorithm for kernel density
estimation (KDE) using HLS and show techniques which were used to optimize the
final FPGA implementation. We are also going to show that FPGA speedups,
comparing to highly optimized CPU and GPU implementations, are quite
substantial. Moreover, power consumption for FPGA devices is usually much less
than typical power consumption of the present CPUs and GPUs.Comment: 23 pages, 6 figures, extended version of initial pape
FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations
Neural network-based methods for image processing are becoming widely used in
practical applications. Modern neural networks are computationally expensive
and require specialized hardware, such as graphics processing units. Since such
hardware is not always available in real life applications, there is a
compelling need for the design of neural networks for mobile devices. Mobile
neural networks typically have reduced number of parameters and require a
relatively small number of arithmetic operations. However, they usually still
are executed at the software level and use floating-point calculations. The use
of mobile networks without further optimization may not provide sufficient
performance when high processing speed is required, for example, in real-time
video processing (30 frames per second). In this study, we suggest
optimizations to speed up computations in order to efficiently use already
trained neural networks on a mobile device. Specifically, we propose an
approach for speeding up neural networks by moving computation from software to
hardware and by using fixed-point calculations instead of floating-point. We
propose a number of methods for neural network architecture design to improve
the performance with fixed-point calculations. We also show an example of how
existing datasets can be modified and adapted for the recognition task in hand.
Finally, we present the design and the implementation of a floating-point gate
array-based device to solve the practical problem of real-time handwritten
digit classification from mobile camera video feed
- …