mirror of
https://github.com/SikongJueluo/Mini-Nav.git
synced 2026-07-12 20:15:31 +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
135 lines
3.6 KiB
Python
135 lines
3.6 KiB
Python
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass
|
|
from typing import Sequence
|
|
import numpy as np
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class MatchResult:
|
|
top1_index: int
|
|
top1_score: int
|
|
scores: np.ndarray
|
|
|
|
|
|
def popcount_int(x: int) -> int:
|
|
return int(x.bit_count())
|
|
|
|
|
|
def mask_width(width: int) -> int:
|
|
return (1 << width) - 1
|
|
|
|
|
|
def xnor_popcount_score(query: int, stored: int, width: int = 512) -> int:
|
|
same_bits = ~(query ^ stored) & mask_width(width)
|
|
return popcount_int(same_bits)
|
|
|
|
|
|
def match_top1(
|
|
query: int,
|
|
rows: Sequence[int],
|
|
*,
|
|
width: int = 512,
|
|
) -> MatchResult:
|
|
"""Pure matching — noise is already baked into rows at write time."""
|
|
scores = np.zeros(len(rows), dtype=np.int32)
|
|
|
|
best_index = 0
|
|
best_score = -1
|
|
|
|
for idx, row in enumerate(rows):
|
|
score = xnor_popcount_score(int(query), int(row), width)
|
|
scores[idx] = score
|
|
|
|
# Tie-break: choose the smallest row index.
|
|
if score > best_score:
|
|
best_score = score
|
|
best_index = idx
|
|
|
|
return MatchResult(
|
|
top1_index=int(best_index),
|
|
top1_score=int(best_score),
|
|
scores=scores,
|
|
)
|
|
|
|
|
|
def match_topk_from_scores(scores: Sequence[int], k: int) -> list[int]:
|
|
"""Return row indices sorted by score desc, row index asc (HW tie-break)."""
|
|
if k <= 0:
|
|
raise ValueError("k must be greater than 0")
|
|
return sorted(range(len(scores)), key=lambda idx: (-int(scores[idx]), idx))[: min(k, len(scores))]
|
|
|
|
|
|
def match_topk(
|
|
query: int,
|
|
rows: Sequence[int],
|
|
*,
|
|
width: int = 512,
|
|
k: int = 5,
|
|
) -> tuple[list[int], np.ndarray]:
|
|
"""Pure Top-K matching — noise is already baked into rows if needed.
|
|
|
|
Returns (list of row indices in rank order, NumPy score array).
|
|
"""
|
|
scores = np.zeros(len(rows), dtype=np.int32)
|
|
for idx, row in enumerate(rows):
|
|
scores[idx] = xnor_popcount_score(int(query), int(row), width)
|
|
return match_topk_from_scores(scores, k), scores
|
|
|
|
|
|
def xorshift128(state: int) -> int:
|
|
"""128-bit xorshift PRNG, single step. Matches random128.sv."""
|
|
mask32 = (1 << 32) - 1
|
|
mask128 = (1 << 128) - 1
|
|
s = state & mask128
|
|
x = (s >> 96) & mask32
|
|
y = (s >> 64) & mask32
|
|
z = (s >> 32) & mask32
|
|
w = s & mask32
|
|
t = (x ^ ((x << 11) & mask32)) & mask32
|
|
next_x = y
|
|
next_y = z
|
|
next_z = w
|
|
next_w = (w ^ (w >> 19) ^ t ^ (t >> 8)) & mask32
|
|
return ((next_x << 96) | (next_y << 64) | (next_z << 32) | next_w) & mask128
|
|
|
|
|
|
def random_hashes(
|
|
rng: np.random.Generator,
|
|
n: int,
|
|
*,
|
|
width: int = 512,
|
|
) -> list[int]:
|
|
words = (width + 63) // 64
|
|
out: list[int] = []
|
|
|
|
for _ in range(n):
|
|
value = 0
|
|
for w in range(words):
|
|
value |= int(rng.integers(0, 1 << 64, dtype=np.uint64)) << (64 * w)
|
|
out.append(value & mask_width(width))
|
|
|
|
return out
|
|
|
|
|
|
def unpack_score_debug_flat(flat: int, num_rows: int, score_bits: int) -> np.ndarray:
|
|
mask = (1 << score_bits) - 1
|
|
return np.array(
|
|
[(int(flat) >> (row * score_bits)) & mask for row in range(num_rows)],
|
|
dtype=np.int32,
|
|
)
|
|
|
|
|
|
def split_hash_to_words_le(value: int, *, width: int = 512, word_bits: int = 32) -> list[int]:
|
|
n_words = width // word_bits
|
|
word_mask = (1 << word_bits) - 1
|
|
return [(int(value) >> (word_bits * i)) & word_mask for i in range(n_words)]
|
|
|
|
|
|
def join_hash_words_le(words: Sequence[int], *, word_bits: int = 32) -> int:
|
|
value = 0
|
|
word_mask = (1 << word_bits) - 1
|
|
for i, word in enumerate(words):
|
|
value |= (int(word) & word_mask) << (word_bits * i)
|
|
return value
|