Re:Microsoft Bets Its Future on a Reprogrammable Computer Chip
by Const-me on Monday September 26, 2016 @12:39AM
(#12579276)
One reason is FPGAs are more flexible.
Sure, GPUs deliver impressive raw performance. To be useful, the task must benefit from massively parallel hardware. GPU hardware works fantastic for shading polygons, training neural networks, or raytracing. For compression and encryption algorithms however, GPUs aren’t terribly good.
Another reason is while a GPU delivers impressive bandwidth on parallel-friendly workloads, it’s usually possible to achieve lower latencies with FPGA. An FPGA doesn’t decode any instructions, and its computing modules exchange data directly.
Sure, GPUs deliver impressive raw performance. To be useful, the task must benefit from massively parallel hardware. GPU hardware works fantastic for shading polygons, training neural networks, or raytracing. For compression and encryption algorithms however, GPUs aren’t terribly good.
Another reason is while a GPU delivers impressive bandwidth on parallel-friendly workloads, it’s usually possible to achieve lower latencies with FPGA. An FPGA doesn’t decode any instructions, and its computing modules exchange data directly.