Video: FPGAs and Machine Learning

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In this video from ATPESC 2019, James Moawad and Greg Nash from Intel present: FPGAs and Machine Learning.

Neural networks are inspired by biological systems, in particular the human brain. Through the combination of powerful computing resources and novel architectures for neurons, neural networks have achieved state-of-the-art results in many domains such as computer vision and machine translation. FPGAs are a natural choice for implementing neural networks as they can handle different algorithms in computing, logic, and memory resources in the same device. Faster performance comparing to competitive implementations as the user can hardcore operations into the hardware. Software developers can use the OpenCL device C level programming standard to target FPGAs as accelerators to standard CPUs without having to deal with hardware level design.

Jim Moawad is a Technical Solution Specialist with Intel’s Network & Custom Logic Group specializing in compute acceleration using Field Programmable Gate Arrays (FPGA). He holds a B.S. in Electrical Engineering from the University of Illinois at Urbana-Champaign and a M.S. in Electrical and Computer Engineering from Georgia Tech with a focus on processor architecture. He designed telecommunication systems at Bell Laboratories / Lucent Technologies from 1999 to 2006 utilizing FPGAs and multi-processor arrays. Since 2006, he has worked as a Field Application Engineer helping customers architect systems with FPGA, embedded processors, DSP and various memory solutions including DRAM, solid state drives and high bandwidth memory (HBM).

Greg Nash is a System Architect for HPC, AI, and Government Analytics in the Military, Aerospace, and Government business group at Intel Programmable Solutions Group.  He is responsible for putting together solutions in these areas between IP, tools, and devices.  Previous to this role, he was a tools specialist for signal processing applications, developing PoC’s and writing papers in FFT’s and radar processing, and designing radio heads at Motorola. He holds a MSEE from UM Ann Arbor.

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