Thread

1. pama · 1789777255 · focus · HN ↗

   Having worked with people doing bringup of specialized chips, I am awed at how the world has changed.

   > When the first chips came back from the foundry in May, the team pointed its internal AI models at designing software to run benchmarks such as SemiAnalysis’s InferenceX. On DeepSeek’s multi-head latent attention kernel benchmark, performance climbed from 0.31 percent of the theoretical ceiling (set by the chip’s compute and memory bandwidth) to 88.94 percent in roughly 40 hours. Ho says this result is repeatable, so the time between when foundries deliver the first chips and when production ramps up can be reduced. “All our schedule assumptions are going to be based on the fact we have this capability now,” he says.

   1. wmf · 1789782465 · focus · HN ↗

      Back in the day you'd write the code before the chip came back but I guess today it's faster to wait.

      1. brookst · 1789826154 · focus · HN ↗

         Makes me wonder about AI and FPGAs. If the cost and effort to (re)program them goes to zero, maybe interesting new applications?

         1. Eridrus · 1789839625 · focus · HN ↗

            I had this same thought and think this is a generally interesting direction, but I think we're in a bit of a weird spot where the compute heavy stuff is on GPUs already and most infra stuff is not compute bound (it's often I/O bound or memory bound in some way).

            It doesn't help that FPGAs are not made at the same scale as CPUs so don't benefit from the economies of scale.

            I'm super curious if you have thoughts on specific pieces of software that would be economically better because I've thought about this in my niche and sort of come to the conclusion that it won't help.

            I do think things like SIMD in CPUs will get more use and maybe we will get more difficult to program for CPU features, but I haven't found a use case where off the shelf FPGA components would help with typical software.

            1. brookst · 1789858682 · focus · HN ↗

               I’m looking at realtime mechanical processes, like shaping extrusion beads from a clay 3d printer. Clay is heterogenous and pressure takes time, so hand tuning is never just right. But put an fpga with vision processing? Seems promising, with millisecond-level latency that I’d never get pushing to a remote system for processing.

               1. Eridrus · 1789932201 · focus · HN ↗

                  Unclear that this sort of thing wouldn't already be quite well served by the existing gpu/npu hardware optimized for neural nets. If you're doing traditional CV, you can run it on the CPU just fine.

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