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

tools/neural_sim/tests/test_neuron.py

@@ -0,0 +1,30 @@
+import numpy as np
+import pytest
+
+from tools.neural_sim.neuron import neuron_scalar, neuron_vectorized
+from tools.neural_sim.numerics import ACT_NONE, ACT_RELU
+
+
+@pytest.mark.parametrize("n_tiles", [1, 2, 4, 16])
+@pytest.mark.parametrize("activation", [ACT_RELU, ACT_NONE])
+@pytest.mark.parametrize("seed", [0, 1, 2, 3, 4])
+def test_scalar_and_vectorized_agree(n_tiles, activation, seed):
+    rng = np.random.default_rng(seed)
+    n = n_tiles * 8
+    inputs = rng.integers(-128, 128, size=n, dtype=np.int64).tolist()
+    weights = rng.integers(-128, 128, size=n, dtype=np.int64).tolist()
+    bias = int(rng.integers(-128, 128))
+
+    scalar = neuron_scalar(inputs, weights, bias=bias, activation=activation)
+    vectorized = neuron_vectorized(inputs, weights, bias=bias, activation=activation)
+    assert scalar == vectorized
+
+
+def test_scalar_and_vectorized_agree_on_extremes():
+    inputs = [-128, 127] * 4
+    weights = [127, -128] * 4
+    assert neuron_scalar(inputs, weights) == neuron_vectorized(inputs, weights)
+
+
+def test_scalar_and_vectorized_agree_on_zero():
+    assert neuron_scalar([0] * 8, [0] * 8) == neuron_vectorized([0] * 8, [0] * 8) == 0