It's a tough space.. seems more economically feasible to just slightly update GPUs for it, similar to what's happening for ML/tensors/etc, and for the rest, FPG

It's a tough space.. seems more economically feasible to just slightly update GPUs for it, similar to what's happening for ML/tensors/etc, and for the rest, FPGAs for prototyping => ASICs for deployment. We were originally building on OpenCL to keep this path open, but a few years later, feels the long-term mainstream + hyperscalar is more like: -- Mainstream: Updated GPUs for general ETL (see: https://rapids.ai https://rapids.ai for replacing spark/pandas/sql/flink/etc. w/ streaming GPU ETL in various convenience formats), w/ assumption of continued hw/sw co-design by Nvidia as more of the data stack than just some neural nets get subsumed -- Some experimental work on FPGAs... -- Hyperscalar / bigshops: ... FPGAs mostly for then getting ASICs for the same thing, e.g., sifive's stack, esp. as cloud vendors figure out asics-as-a-service w/ rapid tapeout & 100X more bandwidth. Still easily a few years away pragmatically tho. But if you're say Netflix and the dev cost of doing this stuff drops say ~10X and you get 1-10% cost savings... more than pays for itself. Wall St. tried GPUs years ago and gave up b/c of usability vs payoff, so FPGAs are still in the middle, yet as tool chain improves, the GPU toolchain is now ~there, and not far for ASICs.