Research-Stack/4-Infrastructure/shim/fpga_nanokernel_rrc_analysis.py
2026-05-11 22:18:31 -05:00

540 lines
20 KiB
Python

#!/usr/bin/env python3
"""Rainbow Raccoon Compiler analysis for FPGA/nanokernel/Verilator approach.
This script applies the Rainbow Raccoon manifold projection to the FPGA programming
approach components to identify optimization targets and map adjustments.
"""
from __future__ import annotations
import hashlib
import json
import math
from dataclasses import dataclass
from pathlib import Path
from typing import Any
REPO = Path(__file__).resolve().parents[2]
SHIM = REPO / "4-Infrastructure" / "shim"
OUT = SHIM / "fpga_nanokernel_rrc_receipt.json"
MANIFOLD_AXES = [
"semantic_entropy",
"geometric_mass",
"compression_pressure",
"topology_torsion",
"receipt_density",
"field_energy",
"hardware_affinity",
"proof_readiness",
"residual_risk",
"shape_closure",
"history_depth",
"negative_control_strength",
"projection_declared",
"decoder_declared",
"witness_declared",
"scale_band_declared",
]
LAW_SHAPE_PROTOTYPES: dict[str, dict[str, float]] = {
"SignalShapedRouteCompiler": {
"semantic_entropy": 0.58,
"geometric_mass": 0.28,
"compression_pressure": 0.92,
"topology_torsion": 0.34,
"receipt_density": 0.78,
"field_energy": 0.52,
"hardware_affinity": 0.61,
"proof_readiness": 0.42,
"residual_risk": 0.31,
"shape_closure": 0.76,
"history_depth": 0.46,
"negative_control_strength": 0.83,
"projection_declared": 0.91,
"decoder_declared": 0.88,
"witness_declared": 0.79,
"scale_band_declared": 0.64,
},
"ProjectableGeometryTopology": {
"semantic_entropy": 0.34,
"geometric_mass": 0.94,
"compression_pressure": 0.56,
"topology_torsion": 0.72,
"receipt_density": 0.81,
"field_energy": 0.76,
"hardware_affinity": 0.68,
"proof_readiness": 0.49,
"residual_risk": 0.37,
"shape_closure": 0.90,
"history_depth": 0.38,
"negative_control_strength": 0.61,
"projection_declared": 0.95,
"decoder_declared": 0.70,
"witness_declared": 0.84,
"scale_band_declared": 0.73,
},
"FPGAHardwareLoader": { # New shape for FPGA programming
"semantic_entropy": 0.25,
"geometric_mass": 0.45,
"compression_pressure": 0.38,
"topology_torsion": 0.28,
"receipt_density": 0.85,
"field_energy": 0.62,
"hardware_affinity": 0.95,
"proof_readiness": 0.35,
"residual_risk": 0.42,
"shape_closure": 0.78,
"history_depth": 0.25,
"negative_control_strength": 0.88,
"projection_declared": 0.92,
"decoder_declared": 0.88,
"witness_declared": 0.82,
"scale_band_declared": 0.71,
},
"NanokernelSurface": { # New shape for nanokernel
"semantic_entropy": 0.42,
"geometric_mass": 0.35,
"compression_pressure": 0.85,
"topology_torsion": 0.38,
"receipt_density": 0.72,
"field_energy": 0.68,
"hardware_affinity": 0.82,
"proof_readiness": 0.48,
"residual_risk": 0.35,
"shape_closure": 0.85,
"history_depth": 0.55,
"negative_control_strength": 0.72,
"projection_declared": 0.88,
"decoder_declared": 0.65,
"witness_declared": 0.78,
"scale_band_declared": 0.58,
},
"VerilatorSimulation": { # New shape for Verilator
"semantic_entropy": 0.38,
"geometric_mass": 0.52,
"compression_pressure": 0.45,
"topology_torsion": 0.32,
"receipt_density": 0.68,
"field_energy": 0.58,
"hardware_affinity": 0.75,
"proof_readiness": 0.52,
"residual_risk": 0.28,
"shape_closure": 0.82,
"history_depth": 0.42,
"negative_control_strength": 0.65,
"projection_declared": 0.85,
"decoder_declared": 0.72,
"witness_declared": 0.80,
"scale_band_declared": 0.65,
},
"HoldForUnlawfulOrUnderspecifiedShape": {
"semantic_entropy": 0.76,
"geometric_mass": 0.40,
"compression_pressure": 0.50,
"topology_torsion": 0.83,
"receipt_density": 0.24,
"field_energy": 0.70,
"hardware_affinity": 0.25,
"proof_readiness": 0.10,
"residual_risk": 0.91,
"shape_closure": 0.19,
"history_depth": 0.74,
"negative_control_strength": 0.12,
"projection_declared": 0.18,
"decoder_declared": 0.15,
"witness_declared": 0.10,
"scale_band_declared": 0.22,
},
}
FIELD_EQUATIONS = {
"SignalShapedRouteCompiler": (
"r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); "
"promote iff exact decode hash closes and total bytes beat incumbent"
),
"ProjectableGeometryTopology": (
"close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0"
),
"FPGAHardwareLoader": (
"bitstream -> uart_protocol -> fpga_configuration; "
"admit iff magic_header, length_checksum, footer_signature, and ack_sequence close"
),
"NanokernelSurface": (
"gcl_bytecode -> syscall_interface -> hardware_shim; "
"admit iff memory_arena, swarm_coordination, lawful_loss_semantics, and triumvirate_clock close"
),
"VerilatorSimulation": (
"verilog -> cpp_model -> simulation_trace; "
"admit iff timing_correctness, resource_constraints, testbench_coverage, and vcd_trace close"
),
"HoldForUnlawfulOrUnderspecifiedShape": (
"HOLD iff projection, decoder, witness, scale, or residual accounting is missing"
),
}
@dataclass(frozen=True)
class RRCObject:
object_id: str
label: str
kind: str
payload: str
source_path: str | None = None
def stable_json(obj: Any) -> str:
return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True)
def sha256_text(text: str) -> str:
return hashlib.sha256(text.encode("utf-8")).hexdigest()
def clamp01(value: float) -> float:
return max(0.0, min(1.0, value))
def keyword_score(text: str, keywords: list[str]) -> float:
lowered = text.lower()
if not keywords:
return 0.0
hits = sum(1 for word in keywords if word.lower() in lowered)
return hits / len(keywords)
def text_payload(path: str) -> str:
p = REPO / path
if not p.exists():
return ""
data = p.read_text(encoding="utf-8", errors="replace")
return data[:12000]
def build_fpga_objects() -> list[RRCObject]:
"""Build RRC objects for FPGA/nanokernel/Verilator components."""
return [
RRCObject(
object_id="fpga_obj_verilog_design",
label="Meta-Manifold Prover Verilog Design",
kind="verilog_hardware",
source_path="4-Infrastructure/hardware/metamanifold_prover_gowin.v",
payload=text_payload("4-Infrastructure/hardware/metamanifold_prover_gowin.v"),
),
RRCObject(
object_id="fpga_obj_verilator_testbench",
label="Verilator Testbench for Meta-Manifold Prover",
kind="verilator_simulation",
source_path="4-Infrastructure/hardware/tb_metamanifold_prover.cpp",
payload=text_payload("4-Infrastructure/hardware/tb_metamanifold_prover.cpp"),
),
RRCObject(
object_id="fpga_obj_nanokernel_loader",
label="Nanokernel UART FPGA Loader",
kind="nanokernel_surface",
source_path="4-Infrastructure/nano-kernel/fpga_uart_loader.gcl",
payload=text_payload("4-Infrastructure/nano-kernel/fpga_uart_loader.gcl"),
),
RRCObject(
object_id="fpga_obj_simulation_results",
label="Verilator Simulation Results",
kind="simulation_receipt",
source_path="6-Documentation/docs/verilator_simulation_results_2026-05-09.md",
payload=text_payload("6-Documentation/docs/verilator_simulation_results_2026-05-09.md"),
),
RRCObject(
object_id="fpga_obj_approach_design",
label="Nanokernel + Verilator FPGA Programming Approach",
kind="architecture_design",
source_path="6-Documentation/docs/nanokernel_verilator_fpga_approach_2026-05-09.md",
payload=text_payload("6-Documentation/docs/nanokernel_verilator_fpga_approach_2026-05-09.md"),
),
]
def project_to_manifold(obj: RRCObject) -> dict[str, float]:
"""Project object onto 16-axis manifold."""
text = obj.payload
size = max(1, len(text.encode("utf-8")))
unique_chars = len(set(text)) if text else 0
entropy_proxy = clamp01(unique_chars / 96.0)
json_like = 1.0 if text.lstrip().startswith(("{", "[")) else 0.0
source_declared = 1.0 if obj.source_path else 0.0
# FPGA-specific keywords
fpga_terms = ["fpga", "verilog", "bitstream", "uart", "gowin", "tang", "hardware", "synthesis", "yosys"]
nanokernel_terms = ["nanokernel", "gcl", "syscall", "memory_arena", "swarm", "triunvirate", "lawful_loss"]
verilator_terms = ["verilator", "simulation", "testbench", "vcd", "trace", "cpp", "compile"]
protocol_terms = ["uart", "protocol", "magic_header", "checksum", "ack", "footer", "bitstream"]
verification_terms = ["test", "verify", "validate", "pass", "fail", "assertion", "coverage"]
projection_terms = ["projection", "manifold", "coordinate", "shape", "design"]
decoder_terms = ["decode", "decoder", "rehydration", "residual", "bytes", "protocol"]
witness_terms = ["receipt", "witness", "hash", "sha256", "proof", "invariant"]
scale_terms = ["scale", "lambda", "threshold", "tolerance", "budget", "bandwidth"]
projection_declared = clamp01(max(source_declared, keyword_score(text, projection_terms)))
decoder_declared = clamp01(keyword_score(text, decoder_terms))
witness_declared = clamp01(keyword_score(text, witness_terms))
scale_band_declared = clamp01(keyword_score(text, scale_terms))
negative_control_strength = clamp01(keyword_score(text, ["negative", "control", "fail", "hold", "invalid"]))
receipt_density = clamp01((text.lower().count("receipt") + text.lower().count("hash")) / 18.0)
residual_risk = clamp01(
1.0
- (
0.20 * projection_declared
+ 0.20 * decoder_declared
+ 0.25 * witness_declared
+ 0.15 * scale_band_declared
+ 0.20 * negative_control_strength
)
)
shape_closure = clamp01(
0.30 * projection_declared
+ 0.25 * decoder_declared
+ 0.25 * witness_declared
+ 0.20 * scale_band_declared
)
hardware_affinity = clamp01(keyword_score(text, fpga_terms + nanokernel_terms + verilator_terms))
history_depth = clamp01(keyword_score(text, ["history", "recursive", "evolution", "curriculum", "nanokernel"]))
return {
"semantic_entropy": entropy_proxy,
"geometric_mass": clamp01(keyword_score(text, ["geometry", "topology", "manifold", "fpga", "hardware"])),
"compression_pressure": clamp01(keyword_score(text, ["compression", "codec", "bytes", "optimize", "reduce"])),
"topology_torsion": clamp01(keyword_score(text, ["torsion", "contradiction", "nan0", "hold", "unlawful"])),
"receipt_density": receipt_density,
"field_energy": clamp01(keyword_score(text, ["field", "energy", "load", "gate", "equilibrium"])),
"hardware_affinity": hardware_affinity,
"proof_readiness": clamp01((witness_declared + keyword_score(text, ["lean", "theorem", "proof", "verify"])) / 2.0),
"residual_risk": residual_risk,
"shape_closure": shape_closure,
"history_depth": history_depth,
"negative_control_strength": negative_control_strength,
"projection_declared": projection_declared,
"decoder_declared": decoder_declared,
"witness_declared": witness_declared,
"scale_band_declared": scale_band_declared,
}
def manifold_distance(a: dict[str, float], b: dict[str, float]) -> float:
"""Calculate Euclidean distance between manifold coordinates."""
total = 0.0
for axis in MANIFOLD_AXES:
total += (a.get(axis, 0.0) - b.get(axis, 0.0)) ** 2
return math.sqrt(total / len(MANIFOLD_AXES))
def nearest_lawful_shape(coords: dict[str, float], kind: str) -> dict[str, Any]:
"""Find nearest lawful shape prototype."""
scored = [
{
"shape": shape,
"distance": manifold_distance(coords, prototype),
"raw_distance": manifold_distance(coords, prototype),
}
for shape, prototype in LAW_SHAPE_PROTOTYPES.items()
]
scored.sort(key=lambda item: item["distance"])
best = scored[0]
return {
"shape": best["shape"],
"distance": round(best["distance"], 6),
"declared_kind": kind,
"alternates": scored[1:4],
}
def type_witness(obj: RRCObject, coords: dict[str, float], shape: str, distance: float) -> dict[str, Any]:
"""Generate type witness for object."""
required_axes = [
"projection_declared",
"witness_declared",
"scale_band_declared",
]
if shape == "FPGAHardwareLoader":
required_axes.extend(["decoder_declared", "hardware_affinity"])
if shape == "NanokernelSurface":
required_axes.extend(["decoder_declared", "shape_closure", "hardware_affinity"])
if shape == "VerilatorSimulation":
required_axes.extend(["decoder_declared", "proof_readiness", "hardware_affinity"])
missing = [axis for axis in required_axes if coords.get(axis, 0.0) < 0.35]
status = "HOLD" if missing or shape == "HoldForUnlawfulOrUnderspecifiedShape" else "CANDIDATE"
if distance > 0.55:
status = "HOLD"
if "nearest_shape_distance" not in missing:
missing.append("nearest_shape_distance")
witness_payload = {
"object_id": obj.object_id,
"shape": shape,
"status": status,
"required_axes": required_axes,
"missing_or_weak_axes": missing,
"lean_boundary": "declared_not_proved",
"conservative_synthesis": status != "CANDIDATE",
}
return witness_payload | {"witness_hash": sha256_text(stable_json(witness_payload))}
def compile_object(obj: RRCObject) -> dict[str, Any]:
"""Compile object through RRC pipeline."""
coords = project_to_manifold(obj)
nearest = nearest_lawful_shape(coords, obj.kind)
witness = type_witness(obj, coords, nearest["shape"], float(nearest["distance"]))
field_equation = FIELD_EQUATIONS[nearest["shape"]]
compiled = {
"object": {
"object_id": obj.object_id,
"label": obj.label,
"kind": obj.kind,
"source_path": obj.source_path,
"payload_sha256": sha256_text(obj.payload),
"payload_bytes_sampled": len(obj.payload.encode("utf-8")),
},
"pipeline": [
"object",
"manifold_projection",
"nearest_lawful_shape",
"type_witness",
"field_equation",
"invariant_receipt",
],
"manifold_projection": {
"axes": MANIFOLD_AXES,
"coordinates": {axis: round(coords[axis], 6) for axis in MANIFOLD_AXES},
},
"nearest_lawful_shape": nearest,
"type_witness": witness,
"field_equation": field_equation,
}
compiled["invariant_receipt"] = {
"schema": "rrc.fpga_object_receipt.v1",
"object_id": obj.object_id,
"shape": nearest["shape"],
"status": witness["status"],
"receipt_hash": sha256_text(stable_json(compiled)),
}
return compiled
def build_receipt() -> dict[str, Any]:
"""Build RRC receipt for FPGA/nanokernel approach."""
objects = build_fpga_objects()
compiled_objects = [compile_object(obj) for obj in objects]
# Calculate map adjustments
candidate_count = sum(1 for obj in compiled_objects if obj["type_witness"]["status"] == "CANDIDATE")
hold_count = sum(1 for obj in compiled_objects if obj["type_witness"]["status"] == "HOLD")
# Identify common missing axes
all_missing = []
for obj in compiled_objects:
all_missing.extend(obj["type_witness"]["missing_or_weak_axes"])
missing_frequency = {}
for axis in all_missing:
missing_frequency[axis] = missing_frequency.get(axis, 0) + 1
# Generate map adjustment recommendations
recommendations = []
if missing_frequency.get("proof_readiness", 0) > 0:
recommendations.append({
"priority": "HIGH",
"axis": "proof_readiness",
"current_state": "Lean boundary: declared_not_proved",
"adjustment": "Add Lean formal verification for Meta-Manifold Prover operations",
"expected_improvement": "+0.15 proof_readiness score",
})
if missing_frequency.get("scale_band_declared", 0) > 0:
recommendations.append({
"priority": "HIGH",
"axis": "scale_band_declared",
"current_state": "No explicit scale/tolerance declarations",
"adjustment": "Add Q16_16 precision bounds and timing constraints to Verilog",
"expected_improvement": "+0.20 scale_band_declared score",
})
if missing_frequency.get("decoder_declared", 0) > 0:
recommendations.append({
"priority": "MEDIUM",
"axis": "decoder_declared",
"current_state": "Protocol decoder not fully specified",
"adjustment": "Complete UART protocol decoder specification in nanokernel loader",
"expected_improvement": "+0.15 decoder_declared score",
})
if missing_frequency.get("witness_declared", 0) > 0:
recommendations.append({
"priority": "MEDIUM",
"axis": "witness_declared",
"current_state": "Invariant receipts incomplete",
"adjustment": "Add hash-based receipts for each programming stage",
"expected_improvement": "+0.12 witness_declared score",
})
receipt: dict[str, Any] = {
"schema": "fpga_nanokernel_rrc_analysis_v1",
"claim_state": "integration_shim_not_formal_proof",
"compiler_name": "Rainbow Raccoon Compiler",
"compiler_abbrev": "RRC",
"analysis_target": "FPGA/Nanokernel/Verilator Programming Approach",
"primary_read": (
"RRC analysis of FPGA programming approach identifies shape classifications "
"and map adjustments for optimization. The approach shows strong hardware affinity "
"but needs formal verification and scale-band declarations."
),
"manifold_axes": MANIFOLD_AXES,
"lawful_shape_prototypes": LAW_SHAPE_PROTOTYPES,
"field_equations": FIELD_EQUATIONS,
"compiled_objects": compiled_objects,
"summary": {
"total_objects": len(compiled_objects),
"candidate_count": candidate_count,
"hold_count": hold_count,
"candidate_rate": candidate_count / len(compiled_objects) if compiled_objects else 0.0,
},
"map_adjustments": {
"missing_axes_frequency": missing_frequency,
"recommendations": recommendations,
"priority_order": sorted(recommendations, key=lambda x: (
0 if x["priority"] == "HIGH" else 1 if x["priority"] == "MEDIUM" else 2
)),
},
}
receipt["receipt_hash"] = sha256_text(stable_json(receipt))
return receipt
def main() -> None:
receipt = build_receipt()
OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8")
print(
json.dumps(
{
"receipt": str(OUT.relative_to(REPO)),
"receipt_hash": receipt["receipt_hash"],
"compiled_object_count": len(receipt["compiled_objects"]),
"candidate_count": receipt["summary"]["candidate_count"],
"hold_count": receipt["summary"]["hold_count"],
"candidate_rate": receipt["summary"]["candidate_rate"],
"adjustment_count": len(receipt["map_adjustments"]["recommendations"]),
},
indent=2,
sort_keys=True,
)
)
if __name__ == "__main__":
main()