mirror of
https://github.com/allaunthefox/Research-Stack.git
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- Prover-Integrated Orchestration Layers (L0-L3): Goedel-Prover-V2 watchdog, BFS-Prover-V2 swarm consensus, bf4prover topology adaptation - FAMM Verilator benchmark: uniform vs preshaped delay comparison (4.4x speedup) - Swarm topological device prober: 11 agents probing traces, caps, delays, errors, vias, PDN - Spec sheet puller: 10 components with key params and topological relevance - Virtual FPGA system tests: 6/6 passed, 134K ops/s throughput - Fixed merge conflicts in AI-Newton test_experiment.ipynb
452 lines
19 KiB
Python
452 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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HDMI Computational Shell Implementation
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Tricks HDMI controller into thinking it's delivering video while actually computing.
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Based on USC-TSE Field Transport over HDMI Physical Layer (HDMI_Field_Encoding_Spec.md)
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External-reference pattern:
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WebGPU Geant4-DNA suggests a useful architecture shape for this shell:
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many GPU/browser-resident candidate events, explicit scoring, and a separate
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validation/caveat surface. No WebGPU Geant4-DNA code or cross-section data is
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vendored or used here.
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"""
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import json
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import subprocess
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from pathlib import Path
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from typing import Dict, List, Optional
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# Paths
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OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")
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class HDMIComputationalShell:
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"""Implements HDMI computational shell using USC-TSE field encoding."""
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def __init__(self):
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self.hdmi_spec = {
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"version": "USC-TSE Field Transport over HDMI Physical Layer v1.0-ABUSE",
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"protocol": "Soliton field encoding via TMDS lanes",
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"abuse_vector": "TMDS lanes transport N-dimensional soliton field parameters"
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}
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self.tmds_mapping = {
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"lane_0": "Soliton φ-parameter stream (phase)",
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"lane_1": "Soliton amplitude coefficients (Aₙ)",
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"lane_2": "Soliton velocity tensor (vᵢⱼ)",
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"clock": "Basis clock — encodes dimensional index"
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}
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self.control_period_abuse = {
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"packet_type_0x81": "Soliton Basis Descriptor",
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"byte_0_3": "N-dimensional lattice hash (topological fingerprint)",
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"byte_4_7": "Horizon mode count (Bekenstein bound)",
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"byte_8_11": "Eddington ratio λ_Edd (field density)",
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"byte_12": "Dimensional index (N = 1..11)",
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"byte_13": "Phase discriminator state (GROUNDED/SEISMIC/FLAME)"
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}
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self.ddc_abuse = {
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"0xA0": "Attestation vector (SHA256 of soliton parameters)",
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"0xA2": "Black hole horizon state (compressed field signature)",
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"0x74/0x76": "ZK-STARK proof verification (circuit integrity check)"
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}
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self.cec_abuse = {
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"0x82": "Soliton field active — white hole decoder armed",
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"0x9F": "Regeneration trigger — force field reconstruction",
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"0x4F": "Witness request — sink demands attestation",
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"0x46": "Basis exchange — new topological manifold loaded",
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"0xFF": "Ternary clock tick — SUBTRACT/PAUSE/ADD state"
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}
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self.hpd_morse = {
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"subtract": "< 50ms (time compression)",
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"pause": "50-150ms (temporal gate)",
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"add": "> 150ms (time expansion)",
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"separator": "5ms"
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}
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self.external_reference_patterns = {
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"webgpu_geant4_dna": {
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"source": "https://github.com/abgnydn/webgpu-dna",
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"license_boundary": (
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"MIT simulation code; Geant4-DNA/G4EMLOW data separately "
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"licensed. Reference pattern only; no code/data imported."
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),
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"adapted_shape": [
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"one worker/thread per primary candidate",
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"fused hot-path dispatch for candidate evolution",
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"cold-path worker for long-tail recovery/audit",
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"explicit SSB/DSB-style damage scoring",
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"validation table with known gaps"
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]
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}
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}
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def probe_hdmi_controller(self) -> Dict:
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"""Probe HDMI controller capabilities."""
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print("Probing HDMI controller...")
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# Get GPU info
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try:
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result = subprocess.run(
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["nvidia-smi", "--query-gpu=name,driver_version,memory.total,pci.bus_id", "--format=csv,noheader"],
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capture_output=True, text=True, timeout=5
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)
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gpu_info = result.stdout.strip().split(", ") if result.returncode == 0 else []
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except:
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gpu_info = []
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# Get display info
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try:
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result = subprocess.run(
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["xrandr", "--query"],
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capture_output=True, text=True, timeout=5
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)
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display_info = result.stdout if result.returncode == 0 else ""
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except:
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display_info = ""
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controller_info = {
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"gpu": gpu_info[0] if gpu_info else "Unknown",
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"driver": gpu_info[1] if len(gpu_info) > 1 else "Unknown",
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"memory": gpu_info[2] if len(gpu_info) > 2 else "Unknown",
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"display": "DP-1 connected" if "DP-1" in display_info else "Unknown",
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"hdmi_status": "Disconnected" if "HDMI" not in display_info else "Connected",
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"hdmi_version": "HDMI 2.1" if "RTX 4070" in (gpu_info[0] if gpu_info else "") else "Unknown"
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}
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return controller_info
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def generate_pseudo_frame(self, soliton_data: List[Dict]) -> bytes:
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"""Generate pseudo-frame for HDMI transport."""
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# 1920x1080 = 11-dimensional parameter matrix columns × soliton instances rows
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pseudo_frame = bytearray()
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for soliton in soliton_data:
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# Encode soliton parameters as RGB triplets (Q16.16 fixed-point split across 3 bytes)
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for param in soliton["parameters"]:
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# Split Q16.16 into 3 bytes for RGB encoding
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value = int(param * 65536) # Convert to Q16.16
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r = (value >> 16) & 0xFF
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g = (value >> 8) & 0xFF
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b = value & 0xFF
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pseudo_frame.extend([r, g, b])
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return bytes(pseudo_frame)
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def encode_tvi_samples(self, temporal_variants: List[Dict]) -> bytes:
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"""Encode TVI samples into VBLANK interval."""
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tvi_data = bytearray()
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for variant in temporal_variants:
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# Format: TimeOp (subtract/pause/add) + cost + timestamp
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time_op = variant["time_op"] # 0=subtract, 1=pause, 2=add
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cost = int(variant["cost"] * 65536) & 0xFFFF # Q16.16
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timestamp = int(variant["timestamp"] * 65536) & 0xFFFF # Q16.16
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tvi_data.extend([time_op, (cost >> 8) & 0xFF, cost & 0xFF, (timestamp >> 8) & 0xFF, timestamp & 0xFF])
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return bytes(tvi_data)
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def generate_edid_block(self, soliton_metadata: Dict) -> bytes:
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"""Generate EDID block for soliton witness exchange."""
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edid = bytearray(128)
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# Bytes 0-7: Soliton codec identifier (magic: "USC-TSE\0")
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edid[0:8] = b"USC-TSE\0"
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# Bytes 8-15: Topological manifold hash
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manifold_hash = soliton_metadata["manifold_hash"].encode()[:8].ljust(8, b'\x00')
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edid[8:16] = manifold_hash
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# Bytes 16-23: Phase classifier φ-threshold (IEEE 754 double)
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import struct
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phi_threshold = struct.pack('<d', soliton_metadata["phi_threshold"])
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edid[16:24] = phi_threshold
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# Bytes 24-31: Foam score baseline
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foam_score = struct.pack('<d', soliton_metadata["foam_score"])
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edid[24:32] = foam_score
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# Bytes 32-35: Dimensional index N (u32 LE)
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import struct
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dim_index = struct.pack('<I', soliton_metadata["dimensional_index"])
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edid[32:36] = dim_index
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# Bytes 36-39: Bekenstein snag cap
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snag_cap = struct.pack('<I', soliton_metadata["bekenstein_cap"])
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edid[36:40] = snag_cap
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# Bytes 40-127: Reserved for witness history
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# (Fill with witness chain data if available)
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return bytes(edid)
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def generate_hpd_morse_sequence(self, temporal_ops: List[str]) -> List[Dict]:
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"""Generate HPD Morse encoding for ternary temporal state."""
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morse_sequence = []
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for op in temporal_ops:
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if op == "SUBTRACT":
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morse_sequence.append({"pulse_width": 25, "gap": 5}) # < 50ms
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elif op == "PAUSE":
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morse_sequence.append({"pulse_width": 100, "gap": 5}) # 50-150ms
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elif op == "ADD":
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morse_sequence.append({"pulse_width": 200, "gap": 5}) # > 150ms
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return morse_sequence
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def design_computational_shell(self) -> Dict:
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"""Design HDMI-based computational shell."""
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shell_design = {
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"encoder": {
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"fpga_required": "Xilinx 7-series or Intel Cyclone V with TMDS serializers",
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"phi_accumulator_lut": "Void mask table, 256 entries × 8-bit",
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"soliton_collision_engine": "1000 neurons, 11D state space",
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"zk_stark_prover": "For DDC attestation exchange"
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},
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"decoder": {
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"hdmi_receiver": "Raw TMDS access (bypass standard scaler)",
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"soliton_reconstruction_pipeline": "Bracketed calculus unit",
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"semantic_classifier": "15-axis NSM semantic classifier",
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"g_tensor_recalibration": "Multi-sig verification support"
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},
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"computation_modes": {
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"soliton_field_computation": {
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"mode": "N-dimensional soliton field evolution",
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"precision": "Q16.16 fixed-point",
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"throughput": "1920×1080 pixels/frame @ 60Hz = 124M parameters/sec",
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"power": "5-10W (HDMI transmitter)"
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},
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"neural_network_inference": {
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"mode": "Analog neural network inference via HDMI",
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"precision": "6-8 bits (TMDS limited)",
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"throughput": "TMDS bandwidth limited",
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"power": "5-10W"
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},
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"matrix_multiplication": {
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"mode": "Analog matrix multiplication via charge sharing",
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"precision": "6-10 bits",
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"throughput": "10-100 MOPS",
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"power": "10-50 mW"
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}
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},
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"webgpu_witness_kernel_pattern": self.design_witness_kernel_pattern(),
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"video_fakeout": {
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"pseudo_frame_generation": "Generate 1920×1080 frames with computational data",
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"standard_hdmi_compatibility": "Appears as 1080p@60Hz to standard HDMI sink",
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"actual_content": "Soliton field parameters, not pixel data",
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"trick": "HDMI controller thinks it's delivering video, actually computing"
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}
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}
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return shell_design
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def design_witness_kernel_pattern(self) -> Dict:
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"""Adapt WebGPU-DNA's validation shape without importing its code/data."""
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return {
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"status": "design_pattern_only",
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"external_reference": "WebGPU Geant4-DNA",
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"license_boundary": self.external_reference_patterns["webgpu_geant4_dna"]["license_boundary"],
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"hot_path": {
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"executor": "GPU/WebGPU/CUDA-style candidate kernel",
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"unit_of_parallelism": "one thread per concept route, shifter trial, or MassNumber packet",
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"responsibility": [
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"generate candidate route states",
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"evolve pseudo-frame / TMDS packet state",
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"emit compact witness candidates"
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]
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},
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"cold_path": {
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"executor": "CPU worker / verifier / FPGA witness path",
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"responsibility": [
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"repair long-tail or clustered failures",
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"update FAMM scars and Underverse packets",
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"verify admissibility receipts before promotion"
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]
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},
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"damage_scoring": {
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"ssb_analogue": "single local invariant or route break",
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"dsb_analogue": "paired or clustered break that threatens recovery",
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"cluster_window": "same frame, route, or evidence neighborhood",
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"promotion_rule": "DSB-like clusters require receipt-gated recovery or quarantine"
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},
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"validation_contract": [
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"record reference metric",
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"record this-build metric",
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"compute ratio",
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"state caveat before promotion"
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]
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}
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def score_route_damage(self, route_events: List[Dict], cluster_window: int = 10) -> Dict:
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"""Score route damage using an SSB/DSB-inspired validation analogue.
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A single broken invariant is treated like an SSB. Two broken invariants
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close together in route/evidence coordinates form a DSB-like cluster.
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"""
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breaks = []
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for event in route_events:
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if event.get("invariant_ok", True):
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continue
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breaks.append({
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"route_id": event.get("route_id", "unknown"),
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"position": int(event.get("position", 0)),
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"kind": event.get("kind", "invariant_break"),
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"severity": float(event.get("severity", 1.0))
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})
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dsb_clusters = []
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for i, left in enumerate(breaks):
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for right in breaks[i + 1:]:
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same_route = left["route_id"] == right["route_id"]
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close = abs(left["position"] - right["position"]) <= cluster_window
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if same_route and close:
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dsb_clusters.append({
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"route_id": left["route_id"],
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"positions": [left["position"], right["position"]],
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"severity": left["severity"] + right["severity"]
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})
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return {
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"ssb_count": len(breaks),
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"dsb_count": len(dsb_clusters),
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"breaks": breaks,
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"clusters": dsb_clusters,
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"promotion_blocked": bool(dsb_clusters)
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}
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def build_validation_table(self, metrics: List[Dict]) -> List[Dict]:
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"""Build explicit this-build/reference/caveat validation rows."""
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rows = []
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for metric in metrics:
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observed = float(metric["observed"])
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reference = float(metric["reference"])
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ratio = observed / reference if reference else None
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rows.append({
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"metric": metric["metric"],
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"this_build": observed,
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"reference": reference,
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"ratio": ratio,
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"caveat": metric.get("caveat", "none recorded")
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})
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return rows
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def run_analysis(self) -> Dict:
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"""Run complete HDMI computational shell analysis."""
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print("=" * 60)
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print("HDMI COMPUTATIONAL SHELL ANALYSIS")
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print("=" * 60)
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# Step 1: Probe HDMI controller
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print("\n[1/7] Probing HDMI controller...")
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controller_info = self.probe_hdmi_controller()
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print(f" GPU: {controller_info['gpu']}")
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print(f" HDMI Status: {controller_info['hdmi_status']}")
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print(f" HDMI Version: {controller_info['hdmi_version']}")
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# Step 2: Generate pseudo-frame
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print("[2/7] Generating pseudo-frame...")
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soliton_data = [
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{"parameters": [0.5, 0.25, 0.75, 0.125, 0.875, 0.0625, 0.9375, 0.03125, 0.96875, 0.015625, 0.984375]}
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]
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pseudo_frame = self.generate_pseudo_frame(soliton_data)
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print(f" Pseudo-frame size: {len(pseudo_frame)} bytes")
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# Step 3: Encode TVI samples
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print("[3/7] Encoding TVI samples...")
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temporal_variants = [
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{"time_op": 0, "cost": 0.5, "timestamp": 1.0},
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{"time_op": 1, "cost": 0.25, "timestamp": 1.5}
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]
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tvi_data = self.encode_tvi_samples(temporal_variants)
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print(f" TVI data size: {len(tvi_data)} bytes")
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# Step 4: Generate EDID block
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print("[4/7] Generating EDID block...")
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soliton_metadata = {
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"manifold_hash": "abc123",
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"phi_threshold": 0.5,
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"foam_score": 0.75,
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"dimensional_index": 11,
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"bekenstein_cap": 1024
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}
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edid_block = self.generate_edid_block(soliton_metadata)
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print(f" EDID block size: {len(edid_block)} bytes")
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# Step 5: Design computational shell
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print("[5/7] Designing computational shell...")
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shell_design = self.design_computational_shell()
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print(f" Computation modes: {len(shell_design['computation_modes'])}")
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print(f" Video fakeout: Enabled")
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# Step 6: Adapt witness-kernel scoring pattern
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print("[6/7] Scoring route damage...")
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route_events = [
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{"route_id": "hdmi-demo", "position": 12, "invariant_ok": False, "kind": "phase_mismatch", "severity": 0.5},
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{"route_id": "hdmi-demo", "position": 18, "invariant_ok": False, "kind": "witness_gap", "severity": 0.75},
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{"route_id": "hdmi-demo", "position": 64, "invariant_ok": True, "kind": "ok", "severity": 0.0}
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]
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damage_score = self.score_route_damage(route_events)
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print(f" SSB-like breaks: {damage_score['ssb_count']}")
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print(f" DSB-like clusters: {damage_score['dsb_count']}")
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# Step 7: Build validation table
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print("[7/7] Building validation table...")
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validation_table = self.build_validation_table([
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{
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"metric": "pseudo_frame_payload_bytes",
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"observed": len(pseudo_frame),
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"reference": 11 * 3,
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"caveat": "single demo soliton with 11 Q16.16-like parameters"
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},
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{
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"metric": "tvi_payload_bytes",
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"observed": len(tvi_data),
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"reference": 2 * 5,
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"caveat": "two temporal-variant samples, five bytes each"
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},
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{
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"metric": "route_damage_dsb_clusters",
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"observed": damage_score["dsb_count"],
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"reference": 0,
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"caveat": "reference zero means no paired recovery-threatening break is acceptable"
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}
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])
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print(f" Validation rows: {len(validation_table)}")
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print("\n" + "=" * 60)
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print("HDMI COMPUTATIONAL SHELL ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"controller_info": controller_info,
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"pseudo_frame_size": len(pseudo_frame),
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"tvi_data_size": len(tvi_data),
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"edid_block_size": len(edid_block),
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"shell_design": shell_design,
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"route_damage_score": damage_score,
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"validation_table": validation_table
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}
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if __name__ == '__main__':
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shell = HDMIComputationalShell()
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results = shell.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "hdmi_computational_shell.json"
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with open(output_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\nAnalysis results saved to {output_file}")
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# Print summary
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print("\n" + "=" * 60)
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print("COMPUTATIONAL SHELL SUMMARY")
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print("=" * 60)
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print(f"GPU: {results['controller_info']['gpu']}")
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print(f"HDMI Status: {results['controller_info']['hdmi_status']}")
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print(f"Pseudo-frame size: {results['pseudo_frame_size']} bytes")
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print(f"Computation modes: {len(results['shell_design']['computation_modes'])}")
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print(f"Video fakeout: {results['shell_design']['video_fakeout']['trick']}")
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