feat(hw): add XNOR-popcount CAM design with cocotb verification

Implement a multi-lane Content Addressable Memory (CAM) that scores
rows by XNOR popcount against a query hash and returns the top-1 match.

RTL modules:
- popcount: parallel group-based population count
- argmax_update: iterative best-match tracking with tie-break
- cam_core: parameterized scanning engine (NUM_ROWS/HASH_BITS/LANES)
  with optional SIM_NOISE and SIM_DEBUG ifdef guards
- cam_top: thin wrapper exposing cam_core ports

Verification:
- Python reference model (ref_model.py) for score-level golden comparison
- cocotb testbench (test_cam_basic.py) covering write/query/reset and
  external noise mask scenarios with score debug verification
- Noise sweep script (sweep_noise.py) measuring top-1 stability under
  configurable bit-flip rates
- Verilator-oriented Makefile with parameterizable compile options

hw/sim/sweep_noise.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import argparse
+
+import numpy as np
+from model.ref_model import match_top1, random_hashes, random_noise_masks
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--rows", type=int, default=512)
+    parser.add_argument("--queries", type=int, default=128)
+    parser.add_argument("--width", type=int, default=512)
+    parser.add_argument("--seed", type=int, default=1234)
+    parser.add_argument(
+        "--rates",
+        type=float,
+        nargs="+",
+        default=[0.0, 0.001, 0.005, 0.01, 0.02, 0.05],
+    )
+    args = parser.parse_args()
+
+    rng = np.random.default_rng(args.seed)
+    rows = random_hashes(rng, args.rows, width=args.width)
+
+    # Construct simple positive queries by selecting existing rows.
+    query_indices = rng.integers(0, args.rows, size=args.queries)
+    queries = [rows[int(i)] for i in query_indices]
+
+    clean_results = [match_top1(q, rows, width=args.width) for q in queries]
+
+    print("rate,top1_stability,avg_clean_margin")
+    for rate in args.rates:
+        stable = 0
+        margins = []
+
+        for q, clean in zip(queries, clean_results):
+            noise_masks = random_noise_masks(
+                rng,
+                args.rows,
+                width=args.width,
+                bit_flip_rate=rate,
+            )
+            noisy = match_top1(q, rows, width=args.width, noise_masks=noise_masks)
+
+            if noisy.top1_index == clean.top1_index:
+                stable += 1
+
+            sorted_scores = np.sort(clean.scores)
+            margin = int(sorted_scores[-1] - sorted_scores[-2])
+            margins.append(margin)
+
+        print(f"{rate},{stable / args.queries:.6f},{np.mean(margins):.3f}")
+
+
+if __name__ == "__main__":
+    main()