The Support Vector Machine (SVM) is a common machine
learning tool that is widely used because of its high classification accuracy . Implementing SVM for embedded real -time applications is very challenging because of the intensi ve computations required. This in creases the
attractiveness of implementing SVM on hardware platforms for reaching high
performance computing with low cost and power consumption.
This paper provides the first comprehensive survey of
current literature (2010- 2015) of different hardware implementation s of
SVM classifier on Field -Programmable Gate Array (FPGA ).
A classification of existing techniques is presented, along with a critical analysis and discussion . A challenging trade -off between
meeting embedded real -time systems constraints
and high classification accuracy has been observed. Finally , some key future research directions are suggested.