Compute Acceleration: GPU or FPGA? New White Paper gives you numbers

[Terry_ALE]

https://forums.xilinx.com/t5/Xcell-Daily-Blog/Compute-Acceleration-GPU-or-FPGA-New-White-Paper-gives...

by sleibso ‎06-14-2017 02:24 PM - edited ‎06-14-2017 02:28 PM(2,234 Views)

Cloud computing and application acceleration for a variety of workloads including big-data analytics, machine learning, video and image processing, and genomics are big data-center topics and if you’re one of those people looking for acceleration guidance, read on. If you’re looking to accelerate compute-intensive applications such as automated driving and ADAS or local video processing and sensor fusion, this blog post’s for you to. The basic problem here is that CPUs are too slow and they burn too much power. You may have one or both of these challenges. If so, you may be considering a GPU or an FPGA as an accelerator in your design.

How to choose?

Although GPUs started as graphics accelerators, primarily for gamers, a few architectural tweaks and a ton of software have made them suitable as general-purpose compute accelerators. With the right software tools, it’s not too difficult to recode and recompile a program to run on a GPU instead of a CPU. With some experience, you’ll find that GPUs are not great for every application workload. Certain computations such as sparse matrix math don’t map onto GPUs well. One big issue with GPUs is power consumption. GPUs aimed at server acceleration in a data-center environment may burn hundreds of watts.

With FPGAs, you can build any sort of compute engine you want with excellent performance/power numbers. You can optimize an FPGA-based accelerator for one task, run that task, and then reconfigure the FPGA if needed for an entirely different application. The amount of computing power you can bring to bear on a problem is scary big. A Virtex UltraScale+ VU13P FPGA can deliver 38.3 INT8 TOPS (that’s tera operations per second) and if you can binarize the application, which is possible with some neural networks, you can hit 500TOPS. That’s why you now see big data-center operators like Baidu and Amazon putting Xilinx-based FPGA accelerator cards into their server farms. That’s also why you see Xilinx offering high-level acceleration programming tools like SDAccel to help you develop compute accelerators using Xilinx All Programmable devices.

For more information about the use of Xilinx devices in such applications including a detailed look at operational efficiency, there’s a new 17-page White Paper titled “Xilinx All Programmable Devices: A Superior Platform for Compute-Intensive Systems.”

[Terry_ALE]

Cloud computing and application acceleration for a variety of workloads including big-data analytics, machine learning, video and image processing, and genomics are big data-center topics and if you’re one of those people looking for acceleration guidance, read on. If you’re looking to accelerate compute-intensive applications such as automated driving and ADAS or local video processing and sensor fusion, this blog post’s for you to. The basic problem here is that CPUs are too slow and they burn too much power. You may have one or both of these challenges. If so, you may be considering a GPU or an FPGA as an accelerator in your design.

How to choose?

Although GPUs started as graphics accelerators, primarily for gamers, a few architectural tweaks and a ton of software have made them suitable as general-purpose compute accelerators. With the right software tools, it’s not too difficult to recode and recompile a program to run on a GPU instead of a CPU. With some experience, you’ll find that GPUs are not great for every application workload. Certain computations such as sparse matrix math don’t map onto GPUs well. One big issue with GPUs is power consumption. GPUs aimed at server acceleration in a data-center environment may burn hundreds of watts.

With FPGAs, you can build any sort of compute engine you want with excellent performance/power numbers. You can optimize an FPGA-based accelerator for one task, run that task, and then reconfigure the FPGA if needed for an entirely different application. The amount of computing power you can bring to bear on a problem is scary big. A Virtex UltraScale+ VU13P FPGA can deliver 38.3 INT8 TOPS (that’s tera operations per second) and if you can binarize the application, which is possible with some neural networks, you can hit 500TOPS. That’s why you now see big data-center operators like Baidu and Amazon putting Xilinx-based FPGA accelerator cards into their server farms. That’s also why you see Xilinx offering high-level acceleration programming tools like SDAccel to help you develop compute accelerators using Xilinx All Programmable devices.

For more information about the use of Xilinx devices in such applications including a detailed look at operational efficiency, there’s a new 17-page White Paper titled “Xilinx All Programmable Devices: A Superior Platform for Compute-Intensive Systems.”