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/examples.py

@@ -0,0 +1,60 @@
+"""
+Small example networks for experimentation (B12). Every example uses
+operations the real V2 accelerator actually implements (INT8 in/INT8
+weight/INT32-wraparound-accumulate/ReLU-saturate-out, P_IN=8 tiles) --
+none invent unsupported functionality. The 2-layer example is
+FPGA-compatible at the computational level via the real dependency-
+graph mechanism (each layer-2 neuron's producer_ids/required fields
+would gate it on all 4 layer-1 neurons completing first), but this
+simulator does not yet model the SPI job-construction/address-wiring
+needed to actually run it end-to-end on hardware (see network.py's
+own scope note).
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from .layer import FCLayer
+from .network import Network
+
+
+def example_8_to_1() -> Network:
+    """8 inputs -> 1 neuron, ReLU. Hand-picked, easy-to-verify weights."""
+    weights = [[1, 2, 3, 4, -1, -2, -3, -4]]
+    return Network([FCLayer(weights, activation="relu")])
+
+
+def example_8_to_4() -> Network:
+    """8 inputs -> 4 neurons, ReLU. Fixed-seed weights for reproducibility."""
+    rng = np.random.default_rng(1)
+    weights = rng.integers(-20, 21, size=(4, 8), dtype=np.int64)
+    return Network([FCLayer(weights, activation="relu")])
+
+
+def example_8_to_16() -> Network:
+    """8 inputs -> 16 neurons, ReLU. Fixed-seed weights."""
+    rng = np.random.default_rng(2)
+    weights = rng.integers(-20, 21, size=(16, 8), dtype=np.int64)
+    return Network([FCLayer(weights, activation="relu")])
+
+
+def example_8_8_1() -> Network:
+    """8 -> 8 -> 1, both layers ReLU. Fixed-seed weights. The hidden
+    layer is 8 wide (not, say, 4) because every layer boundary must
+    stay a multiple of P_IN=8 -- the real hardware always tiles in
+    groups of 8, so a hidden width that doesn't divide evenly would not
+    be a layer this simulator's own FCLayer (or the real accelerator)
+    can actually tile. See module docstring for the FPGA-compatibility
+    scope note re: multi-layer chaining."""
+    rng = np.random.default_rng(3)
+    w1 = rng.integers(-15, 16, size=(8, 8), dtype=np.int64)
+    w2 = rng.integers(-15, 16, size=(1, 8), dtype=np.int64)
+    return Network([FCLayer(w1, activation="relu"), FCLayer(w2, activation="relu")])
+
+
+ALL_EXAMPLES = {
+    "8to1": example_8_to_1,
+    "8to4": example_8_to_4,
+    "8to16": example_8_to_16,
+    "8_8_1": example_8_8_1,
+}