refactor(hw/sim): reorganize CAM top-level tests into per-noise-config suites

Split the monolithic test_cam_basic.py into separate test suites
organized by noise configuration (no_noise, write_noise, read_noise),
with shared utilities extracted to tests/top/utils.py.

- Remove test_cam_basic.py; add no_noise/, write_noise/, read_noise/
  test suites with Makefiles that set noise parameters statically
- Extract helpers (reset_dut, write_rows, query_once, collect_topk,
  etc.) into tests/top/utils.py
- Update hw/sim/Makefile with per-config test targets and a
  test-top-all meta-target
- Update scripts/run_cam_correctness.py to build per-directory
  instead of centrally, removing inline parameter overrides
- Add __init__.py for result_serializer and topk_tracker module tests
- Expand docstrings in test_ref_model_noise.py with architectural
  context and test rationale

hw/sim/tests/top/read_noise/Makefile

@@ -0,0 +1,25 @@
+SIM_ROOT := $(abspath ../../..)
+RTL_ROOT := $(abspath $(SIM_ROOT)/../rtl)
+include $(SIM_ROOT)/mk/rtl-sources.mk
+
+TOPLEVEL := cam_top
+COCOTB_TEST_MODULES := tests.top.read_noise.test_read_noise
+VERILOG_SOURCES := $(RTL_CAM_TOP)
+
+# 读取噪声开启，写入噪声默认关闭
+READ_NOISE_EN := 1
+READ_NOISE_RATE_NUM := 1
+READ_NOISE_RATE_DEN := 100
+WRITE_NOISE_EN := 0
+
+include $(SIM_ROOT)/mk/cocotb-common.mk
+
+# ── 写入+读取双重噪声子目标 ─────────────────────────────────────────
+.PHONY: test-with-write-noise
+
+test-with-write-noise:
+	$(MAKE) -B -f Makefile results.xml WRITE_NOISE_EN=1 \
+	  COCOTB_TEST_FILTER=read_noise_model_match
+
+clean::
+	rm -rf sim_build

hw/sim/tests/top/read_noise/__init__.py

@@ -0,0 +1 @@
+# CAM top-level read-noise integration tests

hw/sim/tests/top/read_noise/test_read_noise.py

@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+"""
+CAM 读取噪声（read_noise）集成测试。
+
+本文件针对 READ_NOISE_EN=1 的编译配置，验证 RTL 的读取噪声行为
+与 Python 参考模型（ref_model）一致。
+
+=== 测试内容 ===
+
+  read_noise_model_match — 读取噪声模型匹配：
+    写入原始哈希，预测含写入噪声（如果 WRITE_NOISE_EN=1）的存储值，
+    再用 match_top1_with_read_noise 计算含读取噪声的期望结果，
+    与 RTL 实际 Top-1 进行比对。
+
+=== 架构背景 ===
+
+  CAM 硬件由以下流水线组成：
+    Write Noise → Banked Core Storage → Read Noise → Match Engine Pipeline
+                                                     ↓
+                                               Top-K Tracker → Result Serializer
+
+  本测试覆盖的是 Read Noise → Match Engine 段。
+  写入噪声（WRITE_NOISE_EN）通过 Makefile 的 test-with-write-noise 子目标
+  启用，测试代码内部已兼容两种配置。
+"""
+
+from __future__ import annotations
+
+import cocotb
+import numpy as np
+from cocotb.clock import Clock
+from cocotb.triggers import RisingEdge
+from model.ref_model import (
+    generate_write_flip_mask,
+    match_top1_with_read_noise,
+    random_hashes,
+    unpack_score_debug_flat,
+)
+from tests.top.utils import (
+    dut_hash_bits,
+    dut_lanes,
+    dut_num_rows,
+    get_param,
+    query_once,
+    reset_dut,
+    write_rows,
+)
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 测试：读取噪声模型匹配
+# ── READ_NOISE_EN=1 由 Makefile 保证，测试代码中不再重复门控
+# ═══════════════════════════════════════════════════════════════════════════════
+
+
+@cocotb.test()
+async def read_noise_model_match(dut):
+    """读取噪声模型匹配：验证 RTL 的读取噪声行为与 Python 参考模型一致。
+
+    与写入噪声不同，读取噪声发生在查询阶段（每次查询向哈希值注入噪声），
+    因此：
+      - 如果先有写入噪声，存储行已经被翻转过一次
+      - 然后查询时还会再注入一层读取噪声
+      - 两层噪声使用不同的种子（写: 0xB504..., 读: 0x6A09...）
+
+    本测试：
+      1. 用 Python 模型预计算存储后的哈希（含写入噪声）
+      2. 用 match_top1_with_read_noise 预计算含读取噪声的期望结果
+      3. 写入原始值到 RTL，查询，比对结果
+    """
+    cocotb.start_soon(Clock(dut.clk, 10, unit="ns").start())
+    await reset_dut(dut)
+
+    num_rows = dut_num_rows(dut)
+    hash_bits = dut_hash_bits(dut)
+    lanes = dut_lanes(dut)
+    rng = np.random.default_rng(123)
+    rows = random_hashes(rng, num_rows, width=hash_bits)
+
+    # If write noise is enabled, apply write flip masks to predict stored rows
+    stored_rows = list(rows)
+    if get_param(dut, "WRITE_NOISE_EN", 0):
+        seed = 0xB504_F32D_B504_F32D
+        prng_state = (seed << 64) | seed
+        stored_rows = []
+        for row in rows:
+            flip, prng_state = generate_write_flip_mask(
+                prng_state,
+                hash_bits,
+                get_param(dut, "WRITE_NOISE_BITS", 8),
+                get_param(dut, "WRITE_NOISE_RATE_NUM", 1),
+                get_param(dut, "WRITE_NOISE_RATE_DEN", 100),
+            )
+            stored_rows.append(row ^ flip)
+
+    query = rows[min(5, num_rows - 1)]
+
+    await write_rows(dut, rows)
+    top1_index, top1_score, score_debug = await query_once(dut, query)
+
+    expected = match_top1_with_read_noise(
+        query,
+        stored_rows,
+        width=hash_bits,
+        lanes=lanes,
+        noise_bits=get_param(dut, "READ_NOISE_BITS", 8),
+        rate_num=get_param(dut, "READ_NOISE_RATE_NUM", 1),
+        rate_den=get_param(dut, "READ_NOISE_RATE_DEN", 100),
+        seed=0x6A09_E667_F3BC_C909,
+    )
+
+    assert top1_index == expected.top1_index
+    assert top1_score == expected.top1_score
+    if score_debug is not None:
+        assert np.array_equal(score_debug, expected.scores)