SEADA introduces an analytical framework combining cost models, mapping tools, and entropy-based precision selection to optimize mixed-precision DNNs on multi-precision spatial architectures.
Mahoney, and Kurt Keutzer
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SOP post-training quantization for LLMs reports lower weight reconstruction error than per-layer FP8 at 1.5 bpw lower cost using per-layer codebook search and hardware-aware formats.
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SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures
SEADA introduces an analytical framework combining cost models, mapping tools, and entropy-based precision selection to optimize mixed-precision DNNs on multi-precision spatial architectures.