ShatterQuant: Breaking Uniform Precision with Block-Wise Mixed-Precision on a Systolic Transformer Hardware Accelerator

arXiv:2610.00207v1 Announce Type: cross Abstract: Due to limited support for intra-tensor heterogeneous precision in conventional accelerators, neural network quantization remains largely restricted to per-tensor precision assignment. We present ShatterQuant, a hardware-software co-designed framework enabling mixed-precision quantization within each tensor by assigning independent bit-widths to blocks of a weight projection. ShatterQuant couples precision granularity with PE configuration, such that each precision determines an effective block height. We introduce (1) a hardware-aware post-tra

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