mirror of
https://github.com/SikongJueluo/Mini-Nav.git
synced 2026-07-12 20:15:31 +08:00
SikongJueluo
cbafc4524e
- Replace NOISE_GEN_BITS/NOISE_SAMPLE_BITS parameters with unified NOISE_BITS
- Use xorshift128 (random128) instead of xorshift64 for PRNG
- Add flip_mask_next combinational helper for single-cycle mask computation
- Add random_enable signal to advance PRNG only on accepted noisy writes
- Simplify FSM by removing mask_group_idx counter
- Update parameter validation: GROUP_BITS (= HASH_BITS/NOISE_BITS) must equal 64
- Update ref_model.py and tests to match new seed convention: {seed, seed}
- Update Makefile and sweep_noise.py with renamed parameters
142 lines
3.7 KiB
Python
142 lines
3.7 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Sequence
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import numpy as np
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@dataclass(frozen=True)
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class MatchResult:
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top1_index: int
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top1_score: int
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scores: np.ndarray
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def popcount_int(x: int) -> int:
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return int(x.bit_count())
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def mask_width(width: int) -> int:
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return (1 << width) - 1
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def xnor_popcount_score(query: int, stored: int, width: int = 512) -> int:
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same_bits = ~(query ^ stored) & mask_width(width)
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return popcount_int(same_bits)
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def match_top1(
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query: int,
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rows: Sequence[int],
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*,
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width: int = 512,
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) -> MatchResult:
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"""Pure matching — noise is already baked into rows at write time."""
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scores = np.zeros(len(rows), dtype=np.int32)
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best_index = 0
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best_score = -1
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for idx, row in enumerate(rows):
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score = xnor_popcount_score(int(query), int(row), width)
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scores[idx] = score
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# Tie-break: choose the smallest row index.
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if score > best_score:
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best_score = score
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best_index = idx
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return MatchResult(
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top1_index=int(best_index),
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top1_score=int(best_score),
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scores=scores,
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)
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def xorshift128(state: int) -> int:
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"""128-bit xorshift PRNG, single step. Matches random128.sv."""
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mask32 = (1 << 32) - 1
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mask128 = (1 << 128) - 1
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s = state & mask128
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x = (s >> 96) & mask32
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y = (s >> 64) & mask32
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z = (s >> 32) & mask32
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w = s & mask32
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t = (x ^ ((x << 11) & mask32)) & mask32
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next_x = y
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next_y = z
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next_z = w
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next_w = (w ^ (w >> 19) ^ t ^ (t >> 8)) & mask32
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return ((next_x << 96) | (next_y << 64) | (next_z << 32) | next_w) & mask128
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def generate_write_flip_mask(
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prng_state: int,
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hash_bits: int,
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noise_bits: int,
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rate_num: int,
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rate_den: int,
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) -> tuple[int, int]:
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"""Generate one write-noise flip mask using one xorshift128 step."""
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assert hash_bits % noise_bits == 0
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group_bits = hash_bits // noise_bits
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bit_index_bits = 6
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sample_bits = 8
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group_random_bits = bit_index_bits + sample_bits
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assert group_bits == 64
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assert noise_bits * group_random_bits <= 128
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sample_range = 1 << sample_bits
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threshold = (rate_num * sample_range) // rate_den
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state = xorshift128(prng_state)
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mask = 0
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for group_idx in range(noise_bits):
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group_rand = (state >> (group_idx * group_random_bits)) & ((1 << group_random_bits) - 1)
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bit_idx = group_rand & ((1 << bit_index_bits) - 1)
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sample = (group_rand >> bit_index_bits) & (sample_range - 1)
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if sample < threshold:
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mask |= 1 << (group_idx * group_bits + bit_idx)
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return mask, state
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def random_hashes(
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rng: np.random.Generator,
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n: int,
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*,
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width: int = 512,
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) -> list[int]:
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words = (width + 63) // 64
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out: list[int] = []
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for _ in range(n):
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value = 0
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for w in range(words):
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value |= int(rng.integers(0, 1 << 64, dtype=np.uint64)) << (64 * w)
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out.append(value & mask_width(width))
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return out
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def unpack_score_debug_flat(flat: int, num_rows: int, score_bits: int) -> np.ndarray:
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mask = (1 << score_bits) - 1
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return np.array(
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[(int(flat) >> (row * score_bits)) & mask for row in range(num_rows)],
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dtype=np.int32,
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)
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def split_hash_to_words_le(value: int, *, width: int = 512, word_bits: int = 32) -> list[int]:
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n_words = width // word_bits
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word_mask = (1 << word_bits) - 1
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return [(int(value) >> (word_bits * i)) & word_mask for i in range(n_words)]
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def join_hash_words_le(words: Sequence[int], *, word_bits: int = 32) -> int:
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value = 0
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word_mask = (1 << word_bits) - 1
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for i, word in enumerate(words):
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value |= (int(word) & word_mask) << (word_bits * i)
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return value
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