docs/rknpu2-campaign-results.md — full campaign lab notebook (E1-E7, pinning,
KV-quant, speculative/MTP, prompt-lookup, int4, driver survey, GEMM ceiling).
ggml/src/ggml-rknpu2/bench/ — standalone rknn_matmul probes used to establish
the hardware ceiling independent of this backend:
mm_peak.c single-context throughput across shapes/layouts/quant types
mm_concurrent.c per-core contexts submitted concurrently (true 3-core rate)
Key result: vendor int8 GEMM tops out at 1.83 TOPS (30.5% of the rated 6.0,
which is a convolution figure); layout and quant type make no difference. The
recoverable prefill headroom is in this backend (CPU glue, per-call overhead),
not in librknnrt.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EWpfhDgYNA21tETDP9ueBE
mfritsche
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