Right. But ... this would limit you to either extremely small models or extremely large FPGA's, yes? If there's a simple machine learning task that requires a sub microsecond latency I can see the point but otherwise??
Right. But ... this would limit you to either extremely small models or extremely large FPGA's, yes? If there's a simple machine learning task that requires a sub microsecond latency I can see the point but otherwise??
Yes, this work is focused on accelerating very small models, typically for real-time systems that require extremely low power or low latency.
One primary application of this work is in high-energy physics (https://home.cern/smarter-decisions-at-the-speed-of-collisio...). Ultrafast and real-time learning is also very applicable for problems in quantum computing, plasma control, etc. (https://arxiv.org/pdf/2602.02005).
Drone target recognition?
I'm not in HFT, but I assume this is also an interesting applicable domain?
The author actually works at Jane Street.
Yes, definitely: this type of work is applicable in domains where software run on general-purpose processors cannot meet latency or power requirements.