$1.58\times -.09\times $ with only a negligible ~0.2% accuracy loss; 2) Mortar-FP8 morph the fp32 weights to fp8 format with a minimal accuracy loss of ~0.3%; and 3) the corresponding hardware accelerator significantly outperforms baselines, achieving up to

$6.032\times $

and

$6.99\times $

performance improvements. The area and power of Mortar are 0.031 mm2 and 68.58 mW. Those metrics are 0.0505 mm2 and 25.16 mW for Mortar-FP8.">

Mortar-FP8: Morphing the Existing FP32 Infrastructure for High-Performance Deep Learning Acceleration (original) (raw)

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