mirror of
https://github.com/SikongJueluo/Mini-Nav.git
synced 2026-07-13 04:25:32 +08:00
SikongJueluo
8b4d4c1b57
- Make cam_read_noise a pass-through module, removing all noise injection logic - Switch write noise to use noise_mask_bernoulli instead of noise_mask_grouped - Add state machine to cam_write_noise for mask generation timing - Remove noise_mask_grouped.sv (no longer needed) - Remove read noise parameters from cam_noisy and cam_top - Update simulation and benchmark code to reflect read noise removal - Sync documentation to reflect Phase 2 architecture
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
"""Sweep write-noise rates and measure top-1 stability.
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Applies write-noise flip masks to stored rows (simulating noisy writes),
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then queries the noisy rows and compares top-1 results against clean rows.
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"""
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from __future__ import annotations
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import argparse
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import sys
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from pathlib import Path
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SIM_ROOT = Path(__file__).resolve().parents[1]
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if str(SIM_ROOT) not in sys.path:
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sys.path.insert(0, str(SIM_ROOT))
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import numpy as np
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from model.ref_model import (
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match_top1,
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random_hashes,
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)
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def apply_write_noise(
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rows: list[int],
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*,
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width: int,
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rate_num: int,
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rate_den: int,
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noise_bits: int = 8,
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seed: int = 0,
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) -> list[int]:
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"""No-op: write-noise flip masks are now generated by Bernoulli RTL only.
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The sweep now measures top-1 stability of pure matching over queries,
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since noise is applied at RTL write time, not in the Python model.
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"""
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return list(rows)
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--rows", type=int, default=512)
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parser.add_argument("--queries", type=int, default=128)
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parser.add_argument("--width", type=int, default=512)
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parser.add_argument("--seed", type=int, default=1234)
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parser.add_argument("--noise-bits", type=int, default=8)
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parser.add_argument(
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"--rates",
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type=float,
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nargs="+",
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default=[0.0, 0.001, 0.005, 0.01, 0.02, 0.05],
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)
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args = parser.parse_args()
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rng = np.random.default_rng(args.seed)
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rows = random_hashes(rng, args.rows, width=args.width)
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# Construct simple positive queries by selecting existing rows.
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query_indices = rng.integers(0, args.rows, size=args.queries)
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queries = [rows[int(i)] for i in query_indices]
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clean_results = [match_top1(q, rows, width=args.width) for q in queries]
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# Use a fixed denominator matching the 8-bit sample space (2^8 = 256).
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# Note: floor() is used, matching RTL threshold = (rate_num * 256) // rate_den.
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# Rates below 1/256 (≈0.39%) collapse to zero under this scheme.
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rate_den = 256
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print("rate,rate_num,effective_prob,top1_stability,avg_clean_margin")
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for rate in args.rates:
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rate_num = int(rate * rate_den)
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effective = rate_num / rate_den if rate_den > 0 else 0.0
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stable = 0
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margins = []
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noisy_rows = apply_write_noise(
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rows,
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width=args.width,
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rate_num=rate_num,
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rate_den=rate_den,
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noise_bits=args.noise_bits,
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seed=args.seed,
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)
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for q, clean in zip(queries, clean_results):
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noisy = match_top1(q, noisy_rows, width=args.width)
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if noisy.top1_index == clean.top1_index:
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stable += 1
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sorted_scores = np.sort(clean.scores)
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margin = int(sorted_scores[-1] - sorted_scores[-2])
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margins.append(margin)
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print(f"{rate},{rate_num},{effective:.6f},{stable / args.queries:.6f},{np.mean(margins):.3f}")
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if __name__ == "__main__":
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main()
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