Accelerating compute with software defined hardware (FPGA's)
summary
FPGAs map custom hardware functions for massive parallelism to cut latency and power while boosting throughput, with claimed data-center wins in performance per watt and scaleable attachment to CPUs. Management uses drivers and APIs so hypervisors and virtual machines can expose FPGAs like CPUs or GPUs. Software access comes via OpenCL and high-level synthesis from C/C++, plus enhanced libraries targeting ML stacks (TensorFlow/Caffe), and a 12‑month push to make this developer-friendly.
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