Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900

crates/ruvector-nervous-system/benches/eprop_bench.rs

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+//! E-prop performance benchmarks
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use ruvector_nervous_system::plasticity::eprop::{EpropNetwork, EpropSynapse};
+
+fn bench_synapse_update(c: &mut Criterion) {
+    let mut group = c.benchmark_group("synapse_update");
+
+    let mut synapse = EpropSynapse::new(0.5, 20.0);
+
+    group.bench_function("single_update", |b| {
+        b.iter(|| {
+            synapse.update(
+                black_box(true),
+                black_box(0.5),
+                black_box(0.1),
+                black_box(1.0),
+                black_box(0.01),
+            );
+        });
+    });
+
+    group.finish();
+}
+
+fn bench_network_forward(c: &mut Criterion) {
+    let mut group = c.benchmark_group("network_forward");
+
+    for &hidden_size in &[100, 500, 1000] {
+        let mut network = EpropNetwork::new(100, hidden_size, 10);
+        let input = vec![0.5; 100];
+
+        group.throughput(Throughput::Elements(hidden_size as u64));
+        group.bench_with_input(
+            BenchmarkId::from_parameter(hidden_size),
+            &hidden_size,
+            |b, _| {
+                b.iter(|| {
+                    network.forward(black_box(&input), black_box(1.0));
+                });
+            },
+        );
+    }
+
+    group.finish();
+}
+
+fn bench_network_backward(c: &mut Criterion) {
+    let mut group = c.benchmark_group("network_backward");
+
+    for &hidden_size in &[100, 500, 1000] {
+        let mut network = EpropNetwork::new(100, hidden_size, 10);
+        let input = vec![0.5; 100];
+        let error = vec![0.1; 10];
+
+        // Run forward first to populate spike buffer
+        network.forward(&input, 1.0);
+
+        group.throughput(Throughput::Elements(hidden_size as u64));
+        group.bench_with_input(
+            BenchmarkId::from_parameter(hidden_size),
+            &hidden_size,
+            |b, _| {
+                b.iter(|| {
+                    network.backward(black_box(&error), black_box(0.01), black_box(1.0));
+                });
+            },
+        );
+    }
+
+    group.finish();
+}
+
+fn bench_online_step(c: &mut Criterion) {
+    let mut group = c.benchmark_group("online_step");
+
+    let mut network = EpropNetwork::new(100, 1000, 10);
+    let input = vec![0.5; 100];
+    let target = vec![1.0; 10];
+
+    group.throughput(Throughput::Elements(1000));
+    group.bench_function("1000_neurons", |b| {
+        b.iter(|| {
+            network.online_step(
+                black_box(&input),
+                black_box(&target),
+                black_box(1.0),
+                black_box(0.01),
+            );
+        });
+    });
+
+    group.finish();
+}
+
+fn bench_memory_footprint(c: &mut Criterion) {
+    let mut group = c.benchmark_group("memory");
+
+    for &size in &[100, 500, 1000, 5000] {
+        group.bench_with_input(BenchmarkId::new("create_network", size), &size, |b, &s| {
+            b.iter(|| {
+                let network = EpropNetwork::new(100, s, 10);
+                black_box(network);
+            });
+        });
+    }
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    bench_synapse_update,
+    bench_network_forward,
+    bench_network_backward,
+    bench_online_step,
+    bench_memory_footprint
+);
+criterion_main!(benches);