mirror of
https://github.com/ruvnet/RuView
synced 2026-08-01 19:01:42 +00:00
ruv
a92b043143
MultistaticArray::fuse / fuse_ungated cloned every viewpoint embedding twice per fusion (once into `extracted`, again when building the attention input). Now the embeddings are MOVED out of `extracted` (one clone per viewpoint instead of two), capturing geometry/ids by Copy in the same pass. Correctness-neutral — all 100 viewpoint/mat lib tests pass unchanged. MEASURED (new benches/fusion_bench.rs, embedding_extract A/B, 8 vp x 128-d): before_double_clone 1.0029 us -> after_single_clone 461.6 ns (~2.17x) End-to-end fusion_pipeline (8 vp): 202 us — marshalling is <1% of fusion (n*n attention dominates), so end-to-end win is modest; the A/B isolates the clone elimination. Reproduce: cargo bench -p wifi-densepose-ruvector --bench fusion_bench Co-Authored-By: claude-flow <ruv@ruv.net>
50 lines
1.5 KiB
TOML
50 lines
1.5 KiB
TOML
[package]
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name = "wifi-densepose-ruvector"
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version = "0.3.1" # ADR-138: ClockQualityGate / clock-quality coherence gate
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edition.workspace = true
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authors.workspace = true
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license.workspace = true
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description = "RuVector v2.0.4 integration layer — ADR-017 signal processing and MAT ruvector integrations"
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repository.workspace = true
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keywords = ["wifi", "csi", "ruvector", "signal-processing", "disaster-detection"]
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categories = ["science", "computer-vision"]
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readme = "README.md"
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[features]
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default = []
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crv = ["dep:ruvector-crv", "dep:ruvector-gnn", "dep:serde", "dep:serde_json"]
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[dependencies]
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ruvector-core = { workspace = true }
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ruvector-mincut = { workspace = true }
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ruvector-attn-mincut = { workspace = true }
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ruvector-temporal-tensor = { workspace = true }
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ruvector-solver = { workspace = true }
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ruvector-attention = { workspace = true }
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ruvector-crv = { workspace = true, optional = true }
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ruvector-gnn = { workspace = true, optional = true }
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thiserror = { workspace = true }
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serde = { workspace = true, optional = true }
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serde_json = { workspace = true, optional = true }
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# ADR-084 Pass 5 — privacy-preserving event log uses SHA-256 to
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# anchor each stored sketch as a content-addressable witness hash.
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sha2 = { workspace = true }
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[dev-dependencies]
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approx = "0.5"
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criterion = { workspace = true }
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[[bench]]
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name = "crv_bench"
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harness = false
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required-features = ["crv"]
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[[bench]]
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name = "sketch_bench"
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harness = false
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[[bench]]
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name = "fusion_bench"
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harness = false
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