mirror of
https://github.com/allaunthefox/Research-Stack.git
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232 lines
10 KiB
Python
232 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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TDMS Controller Computational Repurposing
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Analyzes TDMS (Transition Minimized Differential Signaling) controller for general-purpose computation capabilities.
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Based on HDMI Field Encoding Specification (USC-TSE Field Transport over HDMI Physical Layer).
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"""
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import json
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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 TDMSComputationalController:
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"""Analyzes TDMS controller for general computation based on HDMI Field Encoding Spec."""
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def __init__(self):
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self.tdms_controller = {
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"device": "HDMI TDMS Controller (Transition Minimized Differential Signaling)",
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"lanes": "3 Data + 1 Clock",
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"specification": "USC-TSE Field Transport over HDMI Physical Layer v1.0-ABUSE",
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"computational_potential": "HIGH (soliton field encoding, φ-accumulator, scrambler seed)"
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}
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self.tdms_capabilities = {
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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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"scrambler": "φ-accumulator constant encoding (golden ratio scaled)",
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"cec": "Sympathetic sync channel",
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"hpd": "Morse encoding for ternary temporal state",
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"ddc": "Soliton witness exchange"
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}
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def analyze_computational_potential(self) -> Dict:
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"""Analyze computational potential of TDMS controller."""
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analysis = {
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"tdms_lane_computation": {
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"feasible": True,
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"mode": "TDMS lane computation",
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"description": "Use TDMS lanes for soliton field parameter computation",
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"throughput": "TMDS bandwidth limited (HDMI 2.1: 48 Gbps)",
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"latency": "TMDS symbol rate limited (148.5 MHz for 1080p60)",
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"precision": "8-bit per lane (10-bit TMDS symbol)",
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"power": "5-20W (HDMI controller)",
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"risk": "MEDIUM (requires custom encoder/decoder)"
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},
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"scrambler_seed_computation": {
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"feasible": True,
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"mode": "Scrambler seed computation",
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"description": "Use TMDS scrambler seed for φ-accumulator computation",
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"throughput": "Deterministic quasi-random sequence",
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"latency": "Scrambler update per symbol",
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"precision": "15-bit seed (0x9E37 golden ratio scaled)",
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"power": "5-10W",
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"risk": "LOW-MEDIUM (fixed seed)"
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},
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"cec_sync_computation": {
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"feasible": True,
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"mode": "CEC sync computation",
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"description": "Use CEC for sympathetic sync channel computation",
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"throughput": "CEC bus limited (slow)",
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"latency": "CEC message latency (10-100ms)",
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"precision": "8-bit CEC opcodes",
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"power": "1-3W",
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"risk": "MEDIUM (CEC hijacking)"
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},
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"hpd_morse_computation": {
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"feasible": True,
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"mode": "HPD Morse computation",
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"description": "Use HPD Morse encoding for ternary temporal state",
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"throughput": "Pulse rate limited (5ms separator)",
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"latency": "Pulse width encoding (50-150ms)",
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"precision": "3-state ternary (SUBTRACT/PAUSE/ADD)",
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"power": "1-2W",
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"risk": "LOW (HPD signal manipulation)"
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}
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}
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return analysis
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def design_computational_approach(self) -> Dict:
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"""Design TDMS-based computational approach."""
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approach = {
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"tdms_lane_computation": {
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"concept": "Use TDMS lanes for soliton field computation",
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"implementation": "Encode soliton parameters in TMDS lanes",
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"operations": ["φ-parameter stream", "amplitude coefficients", "velocity tensor"],
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"throughput": "48 Gbps (HDMI 2.1)",
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"latency": "148.5 MHz symbol rate",
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"precision": "8-bit per lane (10-bit TMDS)",
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"power": "5-20W",
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"risk": "MEDIUM"
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},
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"scrambler_computation": {
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"concept": "Use scrambler seed for φ-accumulator",
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"implementation": "Fix scrambler seed to golden ratio constant",
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"operations": ["φ-accumulator", "deterministic quasi-random", "low-discrepancy sequence"],
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"throughput": "Deterministic quasi-random sequence",
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"latency": "Per symbol update",
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"precision": "15-bit seed (0x9E37)",
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"power": "5-10W",
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"risk": "LOW-MEDIUM"
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},
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"cec_computation": {
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"concept": "Use CEC for sync channel computation",
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"implementation": "Hijack CEC opcodes for computation",
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"operations": ["field active", "regeneration trigger", "witness request", "basis exchange", "ternary clock"],
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"throughput": "CEC bus limited",
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"latency": "10-100ms (CEC message)",
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"precision": "8-bit opcodes",
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"power": "1-3W",
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"risk": "MEDIUM"
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},
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"hpd_computation": {
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"concept": "Use HPD for ternary temporal state",
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"implementation": "Encode ternary state in HPD pulse width",
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"operations": ["time compression", "temporal gate", "time expansion"],
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"throughput": "Pulse rate limited (5ms separator)",
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"latency": "50-150ms pulse width",
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"precision": "3-state ternary",
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"power": "1-2W",
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"risk": "LOW"
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}
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}
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return approach
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def estimate_performance(self) -> Dict:
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"""Estimate performance of TDMS controller computation."""
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performance = {
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"tdms_lane": {
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"throughput": "48 Gbps (HDMI 2.1)",
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"latency": "148.5 MHz symbol rate",
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"precision": "8-bit per lane (10-bit TMDS)",
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"operations": "soliton field parameters",
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"power": "5-20W"
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},
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"scrambler": {
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"throughput": "Deterministic quasi-random sequence",
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"latency": "Per symbol update",
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"precision": "15-bit seed (0x9E37)",
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"operations": "φ-accumulator",
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"power": "5-10W"
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},
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"cec": {
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"throughput": "CEC bus limited (slow)",
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"latency": "10-100ms (CEC message)",
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"precision": "8-bit opcodes",
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"operations": "sync channel",
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"power": "1-3W"
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},
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"hpd": {
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"throughput": "Pulse rate limited (5ms separator)",
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"latency": "50-150ms pulse width",
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"precision": "3-state ternary",
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"operations": "ternary temporal state",
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"power": "1-2W"
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}
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}
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return performance
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def run_analysis(self) -> Dict:
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"""Run TDMS controller computational analysis."""
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print("=" * 60)
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print("TDMS CONTROLLER COMPUTATIONAL ANALYSIS")
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print("=" * 60)
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# Step 1: Analyze TDMS controller
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print("\n[1/4] Analyzing TDMS controller...")
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print(f" Device: {self.tdms_controller['device']}")
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print(f" Lanes: {self.tdms_controller['lanes']}")
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print(f" Specification: {self.tdms_controller['specification']}")
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print(f" Computational Potential: {self.tdms_controller['computational_potential']}")
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# Step 2: Analyze computational potential
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print("[2/4] Analyzing computational potential...")
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potential = self.analyze_computational_potential()
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print(f" TDMS Lane: {potential['tdms_lane_computation']['feasible']} - {potential['tdms_lane_computation']['risk']}")
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print(f" Scrambler Seed: {potential['scrambler_seed_computation']['feasible']} - {potential['scrambler_seed_computation']['risk']}")
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print(f" CEC Sync: {potential['cec_sync_computation']['feasible']} - {potential['cec_sync_computation']['risk']}")
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print(f" HPD Morse: {potential['hpd_morse_computation']['feasible']} - {potential['hpd_morse_computation']['risk']}")
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# Step 3: Design computational approach
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print("[3/4] Designing computational approach...")
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approach = self.design_computational_approach()
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print(f" Computational modes: {len(approach)}")
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for mode, details in approach.items():
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print(f" {mode}: {details['throughput']} - {details['risk']}")
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# Step 4: Estimate performance
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print("[4/4] Estimating performance...")
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performance = self.estimate_performance()
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print(f" TDMS Lane: {performance['tdms_lane']['throughput']}")
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print(f" Scrambler: {performance['scrambler']['throughput']}")
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print(f" CEC: {performance['cec']['throughput']}")
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print(f" HPD: {performance['hpd']['throughput']}")
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print("\n" + "=" * 60)
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print("TDMS CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"tdms_controller": self.tdms_controller,
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"tdms_capabilities": self.tdms_capabilities,
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"computational_potential": potential,
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"computational_approach": approach,
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"performance_estimates": performance
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}
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if __name__ == '__main__':
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analyzer = TDMSComputationalController()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "tdms_computational_controller.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("TDMS COMPUTATIONAL CONTROLLER SUMMARY")
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print("=" * 60)
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print(f"Device: {results['tdms_controller']['device']}")
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print(f"Specification: {results['tdms_controller']['specification']}")
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print(f"Computational Potential: {results['tdms_controller']['computational_potential']}")
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print(f"Max Throughput: {results['performance_estimates']['tdms_lane']['throughput']}")
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