28,468 research outputs found
Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA
Neural networks algorithms are commonly used to
recognize patterns from different data sources such as audio or
vision. In image recognition, Convolutional Neural Networks are
one of the most effective techniques due to the high accuracy they
achieve. This kind of algorithms require billions of addition and
multiplication operations over all pixels of an image. However,
it is possible to reduce the number of operations using other
computer vision techniques rather than frame-based ones, e.g.
neuromorphic frame-free techniques. There exists many neuromorphic
vision sensors that detect pixels that have changed
their luminosity. In this study, an event-based convolution engine
for FPGA is presented. This engine models an array of leaky
integrate and fire neurons. It is able to apply different kernel
sizes, from 1x1 to 7x7, which are computed row by row, with a
maximum number of 64 different convolution kernels. The design
presented is able to process 64 feature maps of 7x7 with a latency
of 8.98 s.Ministerio de EconomĂa y Competitividad TEC2016-77785-
- âŠ