Machine Learning and FPGA-Based Hardware Acceleration - Ingrid Funie, Imperial College London 1

The main focus of the Custom Computing research group from Imperial College London is hardware acceleration for a range of applications such as finance, genomics, energy, image recognition and mathematical optimisation. We present how different machine learning techniques are employed in the research world in an attempt to try and provide cutting-edge solutions to a multitude of industrial applications. It is important to emphasise the fact that machine learning is becoming such an important part of the global research community, with a strong presence in many research groups, regardless of their primary areas of expertise.

[@myname8928]

Great Work! One thing GPUs do not have is high-speed transceivers. One can take data directly to FPGA from high-resolution cameras, broadband network, etc. bypassing CPUs and system memory. OpenCL pipes are the best way to do it on FPGAs so far.

[@chipking005]

Appreciate Ingrid for their relentless efforts and work. I hope it will bring mass business to FPGA soon. I have few questions here. 1.Any specific vlsi architecture for processing the data Or simply, tools can do optimize the code from Highlevel program like Java,C,OpenCL to machine level code? 2. What is the role of hardware engineer/FPGA engineer if hardware architecture everything optimized by software?

[@stiiffyrabbit]

Is it part of the 'leap' to FPGAs, that I haven't the first clue how to specify or compare FPGA boards?

[@myname8928]

We can do this for you especially Intel's boards for OpenCL and their board partners.