Lemura AI Labs
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Phase 2 · Next

Hardware acceleration

The same compact detection models compiled onto reconfigurable chips.

The physics problem

Inline inspection is a tax on every request. Serving a detection model on a GPU means buying GPU-shaped infrastructure to run something computationally small, then paying again for every request that arrives.

Reconfigurable chips are a better fit for a classifier than a GPU is: streamlined logic at low precision, rather than general-purpose floating-point hardware.

The target

Line-rate inspection, with order-of-magnitude improvements in latency, power and cost per request. Reprogrammable as threats change — fixed-function silicon would be obsolete the moment the attack surface moved.

The compression and quantization work is the on-ramp: the toolchain that shrinks a model is the one that prepares it for hardware.