feat: neural_sim Python golden functional reference simulator

Adds tools/neural_sim/, a NumPy-based reference implementation of the FPGA-Neural V2 numeric model (INT8 in/weight, 32-bit wraparound accumulation, ReLU+saturate out), derived directly from hardware/v2/rtl/neural_processor.v (not assumed) and reusing tools/validation/mac_oracle.py's own pre-existing, hand-verified two's-complement primitives rather than duplicating them. Provides: neuron/layer/network models, a logical memory model of the real V2 SDRAM map (weights/activations/results), deterministic test-vector generators (simple/signed/extremes/zero/random/D-Stress 256x128) with JSON golden-vector export, an FPGA-vs-Python bit-exact comparison utility, four example networks, a CLI (`python -m tools.neural_sim ...`), and a 96-test pytest suite (all passing) covering signed-arithmetic edge cases (including a direct 32-bit wraparound proof), scalar-vs-vectorized neuron cross-checks, layer/memory/vector/comparison tests. This is a golden functional reference (bit-exact numeric result), explicitly NOT a cycle-accurate FPGA simulator -- see tools/neural_sim/README.md for the full scope statement. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013xXuuRUWZScuo1DeYJxs3v
2026-09-06 19:51:33 +02:00
parent c4763aab10
commit 9b5d1055b8
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+"""
+FPGA-vs-Python comparison utility (B10). Loads FPGA-generated results
+and compares them against this package's own Python golden results.
+The primary pass/fail criterion is 0 mismatches -- exact bit-exact
+match, never a tolerance-based "close enough" comparison (per this
+project's own explicit "for the true bit-exact model, do not hide
+numerical differences behind tolerances" instruction).
+"""
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from typing import List, Optional
+
+
+@dataclass
+class ComparisonReport:
+    exact_match: bool
+    total: int
+    num_mismatches: int
+    first_mismatch_index: Optional[int]
+    first_mismatch_expected: Optional[int]
+    first_mismatch_actual: Optional[int]
+    max_abs_diff: int
+
+    def summary(self) -> str:
+        if self.exact_match:
+            return f"EXACT MATCH: {self.total}/{self.total} outputs bit-exact, 0 mismatches"
+        return (
+            f"MISMATCH: {self.num_mismatches}/{self.total} outputs differ "
+            f"(first at index {self.first_mismatch_index}: "
+            f"expected={self.first_mismatch_expected} actual={self.first_mismatch_actual}, "
+            f"max_abs_diff={self.max_abs_diff})"
+        )
+
+
+def compare_results(expected: List[int], actual: List[int]) -> ComparisonReport:
+    if len(expected) != len(actual):
+        raise ValueError(
+            f"length mismatch: expected has {len(expected)} outputs, "
+            f"actual has {len(actual)} -- cannot compare index-by-index"
+        )
+
+    total = len(expected)
+    num_mismatches = 0
+    first_idx = None
+    first_exp = None
+    first_act = None
+    max_abs_diff = 0
+
+    for i, (e, a) in enumerate(zip(expected, actual)):
+        if e != a:
+            num_mismatches += 1
+            if first_idx is None:
+                first_idx, first_exp, first_act = i, e, a
+            max_abs_diff = max(max_abs_diff, abs(e - a))
+
+    return ComparisonReport(
+        exact_match=(num_mismatches == 0),
+        total=total,
+        num_mismatches=num_mismatches,
+        first_mismatch_index=first_idx,
+        first_mismatch_expected=first_exp,
+        first_mismatch_actual=first_act,
+        max_abs_diff=max_abs_diff,
+    )
+
+
+def load_fpga_results(path: str) -> List[int]:
+    """Loads FPGA-generated results from either a JSON list of ints, or
+    a plain text file with one (whitespace-separated) integer per
+    line -- a common shape for an RTL testbench's own $display/
+    $writememh-style dump."""
+    with open(path) as f:
+        text = f.read()
+    try:
+        data = json.loads(text)
+        if isinstance(data, dict) and "results" in data:
+            data = data["results"]
+        return [int(v) for v in data]
+    except json.JSONDecodeError:
+        return [int(tok) for tok in text.split()]