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Forget struggling with resource constraints: AI Engines on FPGAs can now handle end-to-end scientific neural networks that are too large for programmable logic, unlocking new possibilities for extreme-edge applications.
Replacing softmax with a calibrated clipped-linear approximation can dramatically speed up Transformer inference on edge devices without sacrificing accuracy.
