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https://github.com/SikongJueluo/Mini-Nav.git
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SikongJueluo
f71bf06484
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
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
from __future__ import annotations
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import argparse
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import numpy as np
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from model.ref_model import match_top1, random_hashes, random_noise_masks
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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(
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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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print("rate,top1_stability,avg_clean_margin")
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for rate in args.rates:
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stable = 0
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margins = []
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for q, clean in zip(queries, clean_results):
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noise_masks = random_noise_masks(
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rng,
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args.rows,
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width=args.width,
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bit_flip_rate=rate,
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)
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noisy = match_top1(q, rows, width=args.width, noise_masks=noise_masks)
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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},{stable / args.queries:.6f},{np.mean(margins):.3f}")
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if __name__ == "__main__":
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main()
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