Research-Stack/5-Applications/scripts/pwm_controller_computational.py

231 lines
10 KiB
Python

#!/usr/bin/env python3
"""
PWM Controller Computational Repurposing
Analyzes PWM (Pulse Width Modulation) controllers for general-purpose computation capabilities.
"""
import json
from pathlib import Path
from typing import Dict, List, Optional
# Paths
OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")
class PWMControllerComputational:
"""Analyzes PWM controllers for general computation."""
def __init__(self):
self.pwm_controller = {
"device": "PWM Controller (Pulse Width Modulation)",
"concept": "Use PWM duty cycle and frequency for computation",
"type": "PWM circuits for power regulation, motor control, signal generation",
"frequency_range": "1 Hz - 1 MHz (typical)",
"duty_cycle_range": "0-100%",
"computational_potential": "MEDIUM-HIGH (duty cycle arithmetic, frequency modulation, time-based computation)"
}
self.pwm_capabilities = {
"duty_cycle": "Pulse width modulation (0-100% duty cycle)",
"frequency": "PWM frequency (1 Hz - 1 MHz)",
"phase": "PWM phase shift (0-360 degrees)",
"multiple_channels": "Multiple PWM channels for parallel computation",
"timer_based": "Timer/counter based PWM generation"
}
def analyze_computational_potential(self) -> Dict:
"""Analyze computational potential of PWM controller."""
analysis = {
"duty_cycle_computation": {
"feasible": True,
"mode": "Duty cycle computation",
"description": "Use PWM duty cycle for computational values",
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "PWM period limited (1us - 1s)",
"precision": "8-16 bit duty cycle resolution",
"power": "1-5W (PWM controller)",
"risk": "LOW (non-invasive)"
},
"frequency_modulation": {
"feasible": True,
"mode": "Frequency modulation computation",
"description": "Use PWM frequency for computational encoding",
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "Frequency change latency (1us - 1ms)",
"precision": "Frequency resolution (0.1% - 1%)",
"power": "1-5W",
"risk": "LOW (non-invasive)"
},
"phase_modulation": {
"feasible": True,
"mode": "Phase modulation computation",
"description": "Use PWM phase shift for computational encoding",
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "Phase change latency (1us - 1ms)",
"precision": "8-12 bit phase resolution",
"power": "1-5W",
"risk": "LOW (non-invasive)"
},
"multi_channel_parallel": {
"feasible": True,
"mode": "Multi-channel parallel computation",
"description": "Use multiple PWM channels for parallel computation",
"throughput": "N x frequency (N channels)",
"latency": "PWM period limited",
"precision": "8-16 bit per channel",
"power": "2-10W (multiple channels)",
"risk": "LOW-MEDIUM (channel coordination)"
}
}
return analysis
def design_computational_approach(self) -> Dict:
"""Design PWM controller computational approach."""
approach = {
"duty_cycle_computation": {
"concept": "Use PWM duty cycle for computation",
"implementation": "Encode computational values in duty cycle",
"operations": ["duty cycle arithmetic", "pulse width encoding", "time-based state"],
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "PWM period limited (1us - 1s)",
"precision": "8-16 bit duty cycle",
"power": "1-5W",
"risk": "LOW"
},
"frequency_modulation": {
"concept": "Use PWM frequency for computation",
"implementation": "Encode computational values in frequency",
"operations": ["frequency arithmetic", "modulation encoding", "FM computation"],
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "Frequency change latency (1us - 1ms)",
"precision": "0.1% - 1% frequency resolution",
"power": "1-5W",
"risk": "LOW"
},
"phase_modulation": {
"concept": "Use PWM phase for computation",
"implementation": "Encode computational values in phase shift",
"operations": ["phase arithmetic", "phase encoding", "PM computation"],
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "Phase change latency (1us - 1ms)",
"precision": "8-12 bit phase resolution",
"power": "1-5W",
"risk": "LOW"
},
"multi_channel": {
"concept": "Use multiple PWM channels for parallel computation",
"implementation": "Parallel computation across PWM channels",
"operations": ["parallel duty cycle", "parallel frequency", "channel arithmetic"],
"throughput": "N x frequency (N channels)",
"latency": "PWM period limited",
"precision": "8-16 bit per channel",
"power": "2-10W",
"risk": "LOW-MEDIUM"
}
}
return approach
def estimate_performance(self) -> Dict:
"""Estimate performance of PWM controller computation."""
performance = {
"duty_cycle": {
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "PWM period limited (1us - 1s)",
"precision": "8-16 bit duty cycle",
"operations": "duty cycle arithmetic",
"power": "1-5W"
},
"frequency_modulation": {
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "Frequency change latency (1us - 1ms)",
"precision": "0.1% - 1% frequency resolution",
"operations": "frequency arithmetic",
"power": "1-5W"
},
"phase_modulation": {
"throughput": "Frequency limited (1 Hz - 1 MHz)",
"latency": "Phase change latency (1us - 1ms)",
"precision": "8-12 bit phase resolution",
"operations": "phase arithmetic",
"power": "1-5W"
},
"multi_channel": {
"throughput": "N x frequency (N channels)",
"latency": "PWM period limited",
"precision": "8-16 bit per channel",
"operations": "parallel processing",
"power": "2-10W"
}
}
return performance
def run_analysis(self) -> Dict:
"""Run PWM controller computational analysis."""
print("=" * 60)
print("PWM CONTROLLER COMPUTATIONAL ANALYSIS")
print("=" * 60)
# Step 1: Analyze PWM controller
print("\n[1/4] Analyzing PWM controller...")
print(f" Device: {self.pwm_controller['device']}")
print(f" Concept: {self.pwm_controller['concept']}")
print(f" Type: {self.pwm_controller['type']}")
print(f" Frequency Range: {self.pwm_controller['frequency_range']}")
print(f" Computational Potential: {self.pwm_controller['computational_potential']}")
# Step 2: Analyze computational potential
print("[2/4] Analyzing computational potential...")
potential = self.analyze_computational_potential()
print(f" Duty Cycle: {potential['duty_cycle_computation']['feasible']} - {potential['duty_cycle_computation']['risk']}")
print(f" Frequency Modulation: {potential['frequency_modulation']['feasible']} - {potential['frequency_modulation']['risk']}")
print(f" Phase Modulation: {potential['phase_modulation']['feasible']} - {potential['phase_modulation']['risk']}")
print(f" Multi-Channel: {potential['multi_channel_parallel']['feasible']} - {potential['multi_channel_parallel']['risk']}")
# Step 3: Design computational approach
print("[3/4] Designing computational approach...")
approach = self.design_computational_approach()
print(f" Computational modes: {len(approach)}")
for mode, details in approach.items():
print(f" {mode}: {details['throughput']} - {details['risk']}")
# Step 4: Estimate performance
print("[4/4] Estimating performance...")
performance = self.estimate_performance()
print(f" Duty Cycle: {performance['duty_cycle']['throughput']}")
print(f" Frequency Modulation: {performance['frequency_modulation']['throughput']}")
print(f" Phase Modulation: {performance['phase_modulation']['throughput']}")
print(f" Multi-Channel: {performance['multi_channel']['throughput']}")
print("\n" + "=" * 60)
print("PWM CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE")
print("=" * 60)
return {
"pwm_controller": self.pwm_controller,
"pwm_capabilities": self.pwm_capabilities,
"computational_potential": potential,
"computational_approach": approach,
"performance_estimates": performance
}
if __name__ == '__main__':
analyzer = PWMControllerComputational()
results = analyzer.run_analysis()
# Save results
output_file = OUTPUT_DIR / "pwm_controller_computational.json"
with open(output_file, 'w') as f:
json.dump(results, f, indent=2)
print(f"\nAnalysis results saved to {output_file}")
# Print summary
print("\n" + "=" * 60)
print("PWM CONTROLLER COMPUTATIONAL SUMMARY")
print("=" * 60)
print(f"Device: {results['pwm_controller']['device']}")
print(f"Frequency Range: {results['pwm_controller']['frequency_range']}")
print(f"Computational Potential: {results['pwm_controller']['computational_potential']}")
print(f"Max Throughput: {results['performance_estimates']['multi_channel']['throughput']}")