What’s not shown in the chart is where GPGPUs added to the performance boost. GPGPUs increased the number of cores significantly, bringing a matching rise in computational performance.
Machine learning (ML) is that latest consumer of processor performance—and its appetite seems to be insatiable. Multicore CPUs and GPGPUs have been applied to the problem, and dedicated accelerators are being designed and used when possible, but adapting to new ML models can be a challenge for fixed architecture accelerators. This is one area where FPGAs, with their adaptability, come into play. In particular, embedded FPGAs (eFPGAs) may provide the optimum balance between configurability and performance.
Achronix’s latest eFPGA is Speedcore 7t Gen 4. It includes a number of enhancements tailored to high bandwidth and ML applications. These changes also provide a performance boost for conventional FPGA applications as well.