ECC Analysis Framework

A comprehensive framework for benchmarking, analyzing, and comparing different Error Correction Code (ECC) implementations with support for hardware verification, detailed performance analysis, and parallel processing capabilities.

Features

🚀 Comprehensive Benchmarking

Multiple ECC Types: Support for 25+ ECC schemes including Parity, Hamming SECDED, BCH, Reed-Solomon, CRC, Golay, LDPC, Turbo, Convolutional, Polar, Repetition, Extended Hamming, Product Codes, Concatenated Codes, Reed-Muller, Fire Codes, Spatially-Coupled LDPC, Non-Binary LDPC, Raptor Codes, Adaptive ECC, Burst Error ECC, Three-D Memory ECC, Primary-Secondary ECC, Cyclic ECC, System ECC, and Composite ECC
Flexible Configuration: Test different word lengths (4, 8, 16, 32 bits) and error patterns (single, double, burst, random)
Performance Metrics: Success rates, correction rates, detection rates, code rates, timing analysis
Parallel Execution: Multi-threaded and multi-processed benchmarking for faster results

📋 Supported ECC Types

The framework supports a comprehensive range of Error Correction Codes, each with specific characteristics and use cases:

Basic ECC Codes

ECC Type	Class Name	Error Detection	Error Correction	Code Rate	Use Case
Parity	`ParityECC`	Single-bit errors	None	High	Simple error detection
Repetition	`RepetitionECC`	Multiple-bit errors	Single-bit errors	Low	High reliability, simple implementation
Hamming SECDED	`HammingSECDEDECC`	Double-bit errors	Single-bit errors	Medium	Memory systems, moderate reliability

Advanced ECC Codes

ECC Type	Class Name	Error Detection	Error Correction	Code Rate	Use Case
BCH	`BCHECC`	Multiple-bit errors	Multiple-bit errors	Medium-High	Storage systems, moderate complexity
Reed-Solomon	`ReedSolomonECC`	Burst errors	Burst errors	High	Communication systems, burst error handling
CRC	`CRCECC`	Multiple-bit errors	None (detection only)	Very High	Data integrity checking
Golay	`GolayECC`	Triple-bit errors	Double-bit errors	Medium	Aerospace, high reliability

Modern ECC Codes

ECC Type	Class Name	Error Detection	Error Correction	Code Rate	Use Case
LDPC	`LDPCECC`	Multiple-bit errors	Multiple-bit errors	High	Modern communication, near-Shannon limit
Turbo	`TurboECC`	Multiple-bit errors	Multiple-bit errors	High	3G/4G communications, iterative decoding
Convolutional	`ConvolutionalECC`	Multiple-bit errors	Multiple-bit errors	Medium-High	Wireless communications, streaming data
Polar	`PolarECC`	Multiple-bit errors	Multiple-bit errors	High	5G communications, capacity-achieving

Advanced ECC Codes

ECC Type	Class Name	Error Detection	Error Correction	Code Rate	Use Case
Extended Hamming	`ExtendedHammingECC`	Triple-bit errors	Double-bit errors	Medium	Enhanced memory systems
Product Code	`ProductCodeECC`	Multiple-bit errors	Multiple-bit errors	Medium	High-reliability applications
Concatenated	`ConcatenatedECC`	Multiple-bit errors	Multiple-bit errors	Medium	Multi-layer protection
Reed-Muller	`ReedMullerECC`	Multiple-bit errors	Multiple-bit errors	Medium	Aerospace, high reliability
Fire Code	`FireCodeECC`	Burst errors	Burst errors	Medium-High	Burst error correction
Spatially-Coupled LDPC	`SpatiallyCoupledLDPCECC`	Multiple-bit errors	Multiple-bit errors	High	Advanced communication
Non-Binary LDPC	`NonBinaryLDPCECC`	Multiple-bit errors	Multiple-bit errors	High	Higher rate codes
Raptor Code	`RaptorCodeECC`	Multiple-bit errors	Multiple-bit errors	High	Fountain coding, streaming

Specialized ECC Codes

ECC Type	Class Name	Error Detection	Error Correction	Code Rate	Use Case
Adaptive ECC	`AdaptiveECC`	Dynamic	Dynamic	Variable	Adaptive systems
Burst Error ECC	`BurstErrorECC`	Burst errors	Burst errors	Medium-High	Burst error handling
Three-D Memory ECC	`ThreeDMemoryECC`	Multiple-bit errors	Multiple-bit errors	Medium	3D memory architectures
Primary-Secondary ECC	`PrimarySecondaryECC`	Multiple-bit errors	Multiple-bit errors	Medium	Multi-level protection
Cyclic ECC	`CyclicECC`	Multiple-bit errors	Multiple-bit errors	Medium	Cyclic code applications
System ECC	`SystemECC`	Multiple-bit errors	Multiple-bit errors	Medium	System-level protection
Composite ECC	`CompositeECC`	Multiple-bit errors	Multiple-bit errors	Medium	Composite protection schemes

ECC Characteristics Comparison

Characteristic	Parity	Hamming	BCH	Reed-Solomon	LDPC	Turbo	Polar
Complexity	Very Low	Low	Medium	Medium	High	High	Very High
Latency	Very Low	Low	Medium	Medium	High	High	Very High
Power Efficiency	Very High	High	Medium	Medium	Low	Low	Very Low
Hardware Cost	Very Low	Low	Medium	Medium	High	High	Very High
Error Correction	None	Single-bit	Multi-bit	Burst	Multi-bit	Multi-bit	Multi-bit
Best Error Pattern	Single	Single	Random	Burst	Random	Random	Random

Error Pattern Handling

ECC Type	Single-Bit	Double-Bit	Burst Errors	Random Errors
Parity	✅ Detect	❌	❌	❌
Repetition	✅ Correct	✅ Detect	❌	❌
Hamming SECDED	✅ Correct	✅ Detect	❌	❌
BCH	✅ Correct	✅ Correct	✅ Detect	✅ Correct
Reed-Solomon	✅ Correct	✅ Correct	✅ Correct	✅ Correct
CRC	✅ Detect	✅ Detect	✅ Detect	✅ Detect
Golay	✅ Correct	✅ Correct	✅ Detect	✅ Correct
LDPC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Turbo	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Convolutional	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Polar	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Extended Hamming	✅ Correct	✅ Correct	✅ Detect	✅ Correct
Product Code	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Concatenated	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Reed-Muller	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Fire Code	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Spatially-Coupled LDPC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Non-Binary LDPC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Raptor Code	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Adaptive ECC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Burst Error ECC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Three-D Memory ECC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Primary-Secondary ECC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Cyclic ECC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
System ECC	✅ Correct	✅ Correct	✅ Correct	✅ Correct
Composite ECC	✅ Correct	✅ Correct	✅ Correct	✅ Correct

Performance Trade-offs

ECC Type	Speed	Reliability	Efficiency	Implementation
Parity	⭐⭐⭐⭐⭐	⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Repetition	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐	⭐⭐⭐⭐⭐
Hamming	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
BCH	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Reed-Solomon	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
CRC	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Golay	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
LDPC	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐
Turbo	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐
Convolutional	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Polar	⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐
Adaptive	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Burst Error	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Three-D Memory	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Primary-Secondary	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Cyclic	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
System	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
Composite	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐

Application Recommendations

Application Domain	Recommended ECC	Reasoning
Memory Systems	Hamming SECDED	Good balance of reliability and complexity
Storage Systems	BCH, Reed-Solomon	Excellent burst error handling
Communication	LDPC, Turbo, Polar	Near-optimal performance for noisy channels
Embedded Systems	Parity, CRC	Low complexity, high efficiency
High-Reliability	Golay, LDPC	Maximum error correction capability
High-Speed	Parity, CRC	Minimal latency and overhead
Wireless	Convolutional, Turbo	Excellent for fading channels
5G/6G	Polar	Capacity-achieving codes
Data Integrity	CRC	Fast detection with minimal overhead
Aerospace	Golay, Reed-Solomon	High reliability requirements
Adaptive Systems	Adaptive ECC	Dynamic error correction
Burst Error Channels	Burst Error ECC	Specialized burst handling
3D Memory	Three-D Memory ECC	Optimized for 3D architectures
Multi-Level Protection	Primary-Secondary ECC	Layered error correction
System-Level	System ECC	Comprehensive system protection
Composite Applications	Composite ECC	Multiple protection schemes

Memory Technology ECC Comparison

DDR Generations ECC Evolution

DDR Generation	Primary ECC	Secondary ECC	Error Correction Rate	Bandwidth	Use Case
DDR1 (2000-2003)	Parity	None	~93%	2.1 GB/s	Basic computing
DDR2 (2003-2007)	Hamming SECDED	None	~94-99%	8.5 GB/s	Server/workstation
DDR3 (2007-2014)	Enhanced Hamming	CRC	~99-100%	17 GB/s	High-performance
DDR4 (2014-2020)	Advanced Hamming	CRC + Parity	~99-100%	25.6 GB/s	Modern computing
DDR5 (2020-Present)	On-Die ECC	System Hamming	>99.5%	51.2 GB/s	Next-gen systems

HBM (High Bandwidth Memory) ECC Characteristics

HBM Generation	Primary ECC	Secondary ECC	Error Correction Rate	Bandwidth	Use Case
HBM1 (2015)	On-Die ECC	System Hamming	~99.5%	128 GB/s	Graphics/AI
HBM2 (2016)	Enhanced On-Die	Advanced Hamming	~99.7%	256 GB/s	HPC/AI
HBM2E (2018)	Multi-Layer ECC	BCH ECC	~99.8%	307 GB/s	AI/ML workloads
HBM3 (2022)	Composite ECC	LDPC	~99.9%	819 GB/s	AI/ML workloads
HBM3E (2024)	Advanced Composite	Polar	>99.95%	1.2 TB/s	Next-gen AI

HBM vs DDR ECC Architecture

HBM Multi-Layer ECC Design:

┌─────────────────────────────────────┐
│           HBM Stack                │
├─────────────────────────────────────┤
│  On-Die ECC (Internal)             │
│  - Single-bit error correction     │
│  - Fast local correction           │
├─────────────────────────────────────┤
│  System ECC (External)             │
│  - Multi-bit error correction      │
│  - Advanced codes (LDPC/Polar)     │
├─────────────────────────────────────┤
│  Interface ECC (I/O)               │
│  - Transmission error correction    │
│  - CRC for data integrity          │
└─────────────────────────────────────┘

Key HBM ECC Advantages:

On-Die ECC: Ultra-low latency error correction within memory chips
Multi-Layer Protection: Multiple ECC layers for maximum reliability
Advanced Error Correction: LDPC and Polar codes for near-optimal performance
Bandwidth Efficiency: Minimal impact on memory bandwidth
Power Efficiency: Optimized for high-performance computing

HBM vs DDR ECC Comparison:

Aspect	DDR ECC	HBM ECC
Complexity	Medium	High
Latency	Low	Ultra-low
Bandwidth Impact	Moderate	Minimal
Error Correction	Single-bit	Multi-bit
Power Efficiency	Good	Excellent
Cost	Lower	Higher
Use Case	General computing	AI/ML/HPC
Error Correction Rate	94-100%	99.5-99.95%
Bandwidth	2.1-51.2 GB/s	128-1200 GB/s

Future Memory ECC Trends

AI-Optimized ECC: Specialized codes for AI/ML workloads
Adaptive ECC: Dynamic ECC selection based on error patterns
Quantum-Resistant ECC: Preparing for quantum computing era
Neuromorphic ECC: Brain-inspired error correction algorithms
3D Memory ECC: Specialized codes for stacked memory architectures

📊 Advanced Analysis

Statistical Analysis: Performance rankings, trend analysis, statistical significance testing
Visualization: Comprehensive charts and heatmaps showing performance across different configurations
Scenario-based Recommendations: Best ECC for different use cases (high reliability, high efficiency, high speed, etc.)

🔧 Hardware Verification

Synthesis Support: Yosys integration for hardware cost analysis
Testbench Validation: Verilator integration for functional verification
Conditional Reporting: Only shows hardware results when tools are available

📈 Enhanced Reporting

Data-Driven Reports: Reports based on actual benchmark results
Conditional Sections: Only includes sections when data is available
Multiple Formats: JSON, CSV, and Markdown outputs

⚡ Parallel Processing

Threading: I/O-bound operations with shared memory
Multiprocessing: CPU-intensive operations with true parallelism
Chunked Processing: Memory-efficient processing for large datasets
Auto-detection: Optimal worker count based on system resources

Quick Start

1. Basic Usage

Run the complete analysis pipeline:

# Run full analysis (default)
./run_all.sh

# Run only theoretical analysis
./run_all.sh -m theoretical

# Run only hardware implementation
./run_all.sh -m hardware

# Run hardware implementation without report generation
./run_all.sh -m hardware -s

# Run with verbose output
./run_all.sh -v -m full

2. Python Framework Usage

For direct Python framework usage:

cd src
python run_analysis.py

This will:

Run benchmarks on all available ECC types
Perform hardware verification (if tools are available)
Generate comprehensive analysis and visualizations
Create a detailed report

3. Custom Configuration

Create a custom configuration file (see example_config.json):

{
  "ecc_types": ["ParityECC", "HammingSECDEDECC", "BCHECC"],
  "word_lengths": [8, 16],
  "error_patterns": ["single", "double"],
  "trials_per_config": 10000
}

Run with custom configuration:

python run_analysis.py --config ../example_config.json

4. Selective Execution

Run only specific parts of the pipeline:

# Run only benchmarking
python run_analysis.py --benchmark-only

# Run only hardware verification
python run_analysis.py --hardware-only

# Generate report from existing data
python run_analysis.py --report-only

# Skip hardware verification
python run_analysis.py --skip-hardware

5. Parallel Processing Options

The framework supports multiple parallel processing modes for optimal performance:

# Use multiprocessing for true parallelism (CPU-intensive workloads)
python run_analysis.py --use-processes --workers 8

# Use chunked processing for memory management
python run_analysis.py --chunked --workers 4

# Auto-detect optimal settings
python run_analysis.py --use-processes

# Specify exact number of workers
python run_analysis.py --workers 16

# Performance test different modes
python performance_test.py

6. Performance Testing and Demonstration

Test and demonstrate the parallel processing capabilities:

# Quick performance test (fast)
python quick_test.py

# Concurrent execution demo (visual)
python concurrent_demo.py

# Comprehensive performance analysis
python performance_test.py

# Scalability testing
python performance_test.py --scalability

7. Shell Script Integration

The run_all.sh script provides unified access to all framework features:

# Parallel processing examples
./run_all.sh --use-processes --workers 8
./run_all.sh --chunked --workers 4
./run_all.sh -p auto
./run_all.sh -m benchmark --use-processes

# Performance testing examples
./run_all.sh --performance-test
./run_all.sh --quick-test
./run_all.sh --concurrent-demo
./run_all.sh -m performance

# Analysis and reporting
./run_all.sh -m analysis
./run_all.sh -m benchmark

Setup

Linux/macOS

Clone the repo

Install Python dependencies:

pip install -r requirements.txt
# or for advanced ECCs:
pip install bchlib reedsolo pandas pytest pyldpc commpy seaborn matplotlib scipy psutil

Install Verilator (for hardware simulation):
```
sudo apt-get install verilator
```
Install Yosys (for synthesis):
```
sudo apt-get install yosys
```

Windows (with WSL)

Clone the repo
Run the Windows setup script:
- Double-click run_windows.bat or
- Run run_windows.ps1 in PowerShell
This script will:
- Check if WSL is installed
- Create a Python virtual environment in WSL
- Install required dependencies
- Run the full ECC simulation and analysis

Hardware Requirements

Optional Tools

Yosys: For synthesis and area analysis
Verilator: For testbench simulation and verification

Installation

# Ubuntu/Debian
sudo apt-get install yosys verilator

# macOS
brew install yosys verilator

# Windows
# Download from official websites or use WSL

Usage Guide

Execution Modes

Mode	Description	Use Case
`theoretical`	Python simulation + report generation	Algorithm development, performance comparison
`hardware`	Verilog synthesis + Verilator simulation + report	Hardware implementation verification
`full`	All modes (theoretical + hardware + report)	Complete framework validation
`performance`	Performance testing and parallel processing demo	Performance optimization
`benchmark`	ECC benchmarking only	Focused benchmarking
`analysis`	Analysis and report generation only	Report generation from existing data
`quick-test`	Quick performance test	Framework validation
`concurrent-demo`	Concurrent execution demonstration	Educational demonstration
`design-exploration`	Design space exploration	Primary/secondary ECC combinations

Command Line Options

Shell Script Options (`run_all.sh`)

Option	Long Form	Description
`-m MODE`	`--mode MODE`	Execution mode: `theoretical`, `hardware`, `full`, `performance`, `benchmark`, `analysis`, `quick-test`, `concurrent-demo`, `design-exploration`
`-v`	`--verbose`	Enable verbose output
`-s`	`--skip-report`	Skip report generation (only applicable to hardware mode)
`-p MODE`	`--parallel MODE`	Parallel processing mode: `auto`, `threads`, `processes`, `chunked`
`-w N`	`--workers N`	Number of workers (auto-detect if not specified)
`--use-processes`		Use multiprocessing for true parallelism
`--chunked`		Use chunked processing for memory management
`--performance-test`		Run performance testing and parallel processing demo
`--quick-test`		Run quick performance test
`--concurrent-demo`		Run concurrent execution demonstration
`--overwrite`		Overwrite existing benchmark results
`--with-report`		Generate report after benchmark
`-h`	`--help`	Show help message

Python Script Options (`run_analysis.py`)

Option	Description
`--benchmark-only`	Run only benchmarking
`--hardware-only`	Run only hardware verification
`--report-only`	Generate report from existing data
`--skip-hardware`	Skip hardware verification
`--config FILE`	Use custom configuration file
`--output DIR`	Output directory for results
`--use-processes`	Use ProcessPoolExecutor instead of ThreadPoolExecutor
`--workers N`	Number of workers (auto-detect if not specified)
`--chunked`	Use chunked processing to manage memory better
`--memory-limit FLOAT`	Memory usage limit as fraction of total RAM
`--overwrite`	Overwrite existing benchmark results

Configuration Options

Benchmark Configuration

Parameter	Type	Default	Description
`ecc_types`	List[str]	All available	ECC classes to test
`word_lengths`	List[int]	[4, 8, 16, 32]	Data word lengths to test
`error_patterns`	List[str]	["single", "double", "burst", "random"]	Error injection patterns
`trials_per_config`	int	10000	Number of trials per configuration
`burst_length`	int	3	Length of burst errors
`random_error_prob`	float	0.01	Probability for random errors
`measure_timing`	bool	True	Enable timing measurements
`max_workers`	int	4	Number of parallel workers

Error Patterns

single: Single bit errors
double: Double bit errors
burst: Consecutive bit errors
random: Random bit errors with specified probability

Output Files

Benchmark Results

benchmark_results.json: Detailed benchmark data
benchmark_summary.json: Summary statistics
benchmark_results.csv: CSV format for external analysis

Analysis Visualizations

ecc_performance_analysis.png: Overall performance comparison
ecc_performance_heatmap.png: Performance heatmap by error pattern
ecc_word_length_trends.png: Performance trends vs word length

Hardware Verification

hardware_verification.json: Synthesis and testbench results

Final Report

ecc_analysis_report.md: Comprehensive analysis report

Logs

results/run.log: Complete execution log
results/*/simulation.log: Individual testbench logs (hardware mode)

Individual Testbench Execution

# List available testbenches
python3 src/verilate_single.py --list

# Run specific testbench
python3 src/verilate_single.py <testbench_name>

Execution Plan and Logging

The shell script provides comprehensive execution planning and logging:

Execution Plan: Shows mode, verbose settings, parallel processing options, and worker configuration
Section Headers: Clear visual separation of different execution phases
Progress Tracking: Real-time progress updates during execution
Comprehensive Logging: All output is logged to results/run.log
Completion Summary: Detailed summary of what was executed and where results are located
Debugging Information: Individual testbench logs and debugging commands

Performance Metrics

Primary Metrics

Success Rate: Percentage of successful error handling
Correction Rate: Percentage of errors corrected
Detection Rate: Percentage of errors detected
Code Rate: Data efficiency (data bits / total bits)
Overhead Ratio: Redundancy overhead

Timing Metrics

Encode Time: Time to encode data
Decode Time: Time to decode and correct
Total Time: Combined encoding and decoding time

Hardware Metrics

Area (Cells): Synthesis area in logic cells
Relative Cost: Cost relative to smallest implementation
Power Estimate: Estimated power consumption

Framework Architecture

Core Modules

1. Benchmark Suite (`benchmark_suite.py`)

Purpose: Comprehensive ECC performance testing
Features:
- Multi-threaded and multi-processed execution
- Configurable test parameters
- Multiple error injection patterns
- Performance timing measurements
- Incremental result saving
- Memory-efficient chunked processing

2. Enhanced Analysis (`enhanced_analysis.py`)

Purpose: Statistical analysis and visualization
Features:
- Performance rankings
- Trend analysis
- Statistical significance testing
- Automated chart generation
- ECC implementation verification
- Parallel verification processing

3. Parallel Processing (`run_analysis.py`)

Purpose: High-performance execution with multiple parallel modes
Features:
- Threading: I/O-bound operations with shared memory
- Multiprocessing: CPU-intensive operations with true parallelism
- Chunked Processing: Memory-efficient processing for large datasets
- Auto-detection: Optimal worker count based on system resources
- Progress Tracking: Real-time progress monitoring

4. Hardware Verification (`hardware_verification.py`)

Purpose: Hardware implementation validation
Features:
- Yosys synthesis integration
- Verilator testbench validation
- Tool availability detection
- Conditional result reporting
- Python ECC implementation verification

5. Report Generator (`report_generator.py`)

Purpose: Comprehensive report generation
Features:
- Data-driven content
- Conditional sections
- Multiple visualization formats
- Professional formatting

6. Main Orchestrator (`run_analysis.py`)

Purpose: Pipeline coordination and CLI interface
Features:
- Command-line argument parsing
- Pipeline orchestration
- Error handling and recovery
- Progress reporting

Data Flow

1. Configuration → 2. Benchmarking → 3. Analysis → 4. Hardware Verification → 5. Report Generation
     ↓                    ↓              ↓              ↓                        ↓
   JSON Config    Benchmark Results   Analysis    Hardware Results        Final Report
                                    Results

Advanced Usage

Advanced ECC Analysis

Statistical Analysis and Performance Evaluation

The framework provides comprehensive statistical analysis capabilities for evaluating ECC performance across different scenarios:

# Advanced statistical analysis
from enhanced_analysis import ECCAnalyzer

# Load benchmark results
analyzer = ECCAnalyzer(benchmark_results)

# Performance rankings
rankings = analyzer.analyze_performance_rankings()
print("ECC Performance Rankings:", rankings)

# Scenario-based analysis
scenarios = analyzer.analyze_scenario_performance()
print("Best ECC for Different Scenarios:", scenarios)

# Trend analysis
word_length_trends = analyzer.analyze_word_length_trends()
error_pattern_trends = analyzer.analyze_error_pattern_trends()

# Statistical significance testing
significance = analyzer.analyze_statistical_significance()

Error Pattern Analysis

Advanced error pattern analysis helps understand ECC behavior under different error conditions:

# Error pattern analysis
def analyze_error_patterns(ecc_results):
    """Analyze error patterns and their impact on ECC performance."""
    patterns = {
        'systematic': {'rate': 0.6, 'impact': 'high'},
        'burst': {'rate': 0.25, 'impact': 'medium'},
        'random': {'rate': 0.15, 'impact': 'low'}
    }
    
    # Pattern-specific ECC recommendations
    recommendations = {
        'systematic': 'Use SystematicErrorECC or BCH',
        'burst': 'Use BurstErrorECC or Reed-Solomon',
        'random': 'Use LDPC or Turbo codes'
    }
    
    return patterns, recommendations

Performance Optimization Strategies

# Performance optimization
class OptimizedECCAnalyzer:
    def __init__(self, benchmark_results):
        self.results = benchmark_results
        self.optimization_strategies = {
            'speed': self._optimize_for_speed,
            'reliability': self._optimize_for_reliability,
            'efficiency': self._optimize_for_efficiency
        }
    
    def _optimize_for_speed(self):
        """Optimize for maximum speed."""
        return sorted(self.results, key=lambda x: x.encode_time_avg + x.decode_time_avg)
    
    def _optimize_for_reliability(self):
        """Optimize for maximum reliability."""
        return sorted(self.results, key=lambda x: x.correction_rate, reverse=True)
    
    def _optimize_for_efficiency(self):
        """Optimize for maximum efficiency."""
        return sorted(self.results, key=lambda x: x.code_rate, reverse=True)

Machine Learning Integration

# ML-based ECC selection
import numpy as np
from sklearn.ensemble import RandomForestClassifier

class MLECCAnalyzer:
    def __init__(self):
        self.classifier = RandomForestClassifier()
        self.feature_names = ['word_length', 'error_rate', 'burst_prob', 'systematic_prob']
    
    def train_model(self, training_data):
        """Train ML model for ECC selection."""
        X = training_data[self.feature_names]
        y = training_data['optimal_ecc']
        self.classifier.fit(X, y)
    
    def predict_optimal_ecc(self, features):
        """Predict optimal ECC based on features."""
        return self.classifier.predict([features])[0]
    
    def get_feature_importance(self):
        """Get feature importance for ECC selection."""
        return dict(zip(self.feature_names, self.classifier.feature_importances_))

Design Space Exploration

Primary-Secondary ECC Combinations

The framework supports exploration of multi-level ECC architectures:

# Design space exploration
class ECCDesignExplorer:
    def __init__(self):
        self.primary_eccs = ['ParityECC', 'HammingSECDEDECC', 'BCHECC']
        self.secondary_eccs = ['ReedSolomonECC', 'LDPCECC', 'TurboECC']
        self.combinations = []
    
    def explore_combinations(self):
        """Explore all primary-secondary ECC combinations."""
        for primary in self.primary_eccs:
            for secondary in self.secondary_eccs:
                combination = {
                    'primary': primary,
                    'secondary': secondary,
                    'performance': self._evaluate_combination(primary, secondary)
                }
                self.combinations.append(combination)
        
        return sorted(self.combinations, key=lambda x: x['performance']['overall_score'], reverse=True)
    
    def _evaluate_combination(self, primary, secondary):
        """Evaluate performance of ECC combination."""
        return {
            'error_correction_rate': 0.95,
            'overhead_ratio': 0.2,
            'latency_impact': 0.15,
            'overall_score': 0.85
        }

Multi-Objective Optimization

# Multi-objective optimization
from scipy.optimize import minimize

class MultiObjectiveECCOptimizer:
    def __init__(self, ecc_types, constraints):
        self.ecc_types = ecc_types
        self.constraints = constraints
    
    def optimize(self, objectives):
        """Optimize ECC selection for multiple objectives."""
        def objective_function(x):
            # x represents ECC parameters
            reliability = self._calculate_reliability(x)
            efficiency = self._calculate_efficiency(x)
            speed = self._calculate_speed(x)
            
            # Weighted sum of objectives
            return -(0.4 * reliability + 0.3 * efficiency + 0.3 * speed)
        
        # Constraints
        constraints = [
            {'type': 'ineq', 'fun': lambda x: x[0] - 0.8},  # Minimum reliability
            {'type': 'ineq', 'fun': lambda x: 0.3 - x[1]},   # Maximum overhead
            {'type': 'ineq', 'fun': lambda x: x[2] - 0.7}    # Minimum speed
        ]
        
        result = minimize(objective_function, x0=[0.9, 0.2, 0.8], constraints=constraints)
        return result

Adaptive ECC Architecture

# Adaptive ECC architecture
class AdaptiveECCArchitecture:
    def __init__(self, base_ecc_types):
        self.base_ecc_types = base_ecc_types
        self.current_ecc = None
        self.performance_history = []
    
    def adapt_to_conditions(self, current_conditions):
        """Adapt ECC based on current conditions."""
        optimal_ecc = self._select_optimal_ecc(current_conditions)
        
        if optimal_ecc != self.current_ecc:
            self._switch_ecc(optimal_ecc)
            self.current_ecc = optimal_ecc
        
        return self.current_ecc
    
    def _select_optimal_ecc(self, conditions):
        """Select optimal ECC based on conditions."""
        error_rate = conditions.get('error_rate', 0.01)
        latency_requirement = conditions.get('latency_requirement', 'medium')
        power_constraint = conditions.get('power_constraint', 'medium')
        
        if error_rate > 0.1:
            return 'LDPCECC'  # High error correction
        elif latency_requirement == 'low':
            return 'ParityECC'  # Fast detection
        elif power_constraint == 'low':
            return 'HammingSECDEDECC'  # Balanced
        else:
            return 'BCHECC'  # Good balance

3D Memory ECC Optimization

# 3D Memory ECC optimization
class ThreeDMemoryECCOptimizer:
    def __init__(self, layers, bits_per_layer):
        self.layers = layers
        self.bits_per_layer = bits_per_layer
        self.layer_eccs = []
    
    def optimize_layer_eccs(self):
        """Optimize ECC for each layer of 3D memory."""
        for layer in range(self.layers):
            layer_ecc = self._select_layer_ecc(layer)
            self.layer_eccs.append(layer_ecc)
        
        return self.layer_eccs
    
    def _select_layer_ecc(self, layer):
        """Select optimal ECC for specific layer."""
        if layer == 0:  # Bottom layer - highest reliability needed
            return 'ExtendedHammingECC'
        elif layer < self.layers // 2:  # Middle layers - balanced
            return 'BCHECC'
        else:  # Top layers - speed optimized
            return 'HammingSECDEDECC'

Burst Error Handling Optimization

# Burst error handling optimization
class BurstErrorOptimizer:
    def __init__(self, burst_lengths, error_rates):
        self.burst_lengths = burst_lengths
        self.error_rates = error_rates
    
    def optimize_burst_handling(self):
        """Optimize burst error handling strategies."""
        strategies = {
            'short_burst': self._optimize_short_burst,
            'medium_burst': self._optimize_medium_burst,
            'long_burst': self._optimize_long_burst
        }
        
        results = {}
        for burst_type, optimizer in strategies.items():
            results[burst_type] = optimizer()
        
        return results
    
    def _optimize_short_burst(self):
        """Optimize for short burst errors (1-3 bits)."""
        return {
            'recommended_ecc': 'HammingSECDEDECC',
            'correction_rate': 0.99,
            'overhead': 0.15
        }
    
    def _optimize_medium_burst(self):
        """Optimize for medium burst errors (4-8 bits)."""
        return {
            'recommended_ecc': 'BurstErrorECC',
            'correction_rate': 0.95,
            'overhead': 0.25
        }
    
    def _optimize_long_burst(self):
        """Optimize for long burst errors (9+ bits)."""
        return {
            'recommended_ecc': 'ReedSolomonECC',
            'correction_rate': 0.90,
            'overhead': 0.35
        }

System-Level ECC Integration

# System-level ECC integration
class SystemLevelECCOptimizer:
    def __init__(self, system_components):
        self.components = system_components
        self.system_ecc = {}
    
    def optimize_system_ecc(self):
        """Optimize ECC for entire system."""
        for component, requirements in self.components.items():
            optimal_ecc = self._select_component_ecc(requirements)
            self.system_ecc[component] = optimal_ecc
        
        return self.system_ecc
    
    def _select_component_ecc(self, requirements):
        """Select optimal ECC for system component."""
        reliability = requirements.get('reliability', 'medium')
        speed = requirements.get('speed', 'medium')
        power = requirements.get('power', 'medium')
        
        if reliability == 'high' and speed == 'high':
            return 'ExtendedHammingECC'
        elif reliability == 'high':
            return 'LDPCECC'
        elif speed == 'high':
            return 'ParityECC'
        else:
            return 'HammingSECDEDECC'

Approximate Computing and ECC Integration

The framework supports integration with approximate computing systems, particularly for FIR multipliers and other approximate arithmetic units:

# Approximate computing ECC integration
class ApproximateComputingECC:
    def __init__(self, word_length: int, error_model: Dict):
        self.word_length = word_length
        self.error_model = error_model
        self.approximate_ecc = self._select_approximate_ecc()
    
    def _select_approximate_ecc(self) -> ECCBase:
        """Select ECC optimized for approximate computing."""
        error_pattern = self.error_model.get('pattern', 'systematic')
        
        if error_pattern == 'systematic':
            return SystematicErrorECC(self.word_length, self.error_model)
        elif error_pattern == 'burst':
            return BurstErrorECC(self.word_length, burst_length=3)
        else:
            return AdaptiveECC(self.word_length)
    
    def encode(self, data: int) -> int:
        """Encode data for approximate computing protection."""
        return self.approximate_ecc.encode(data)
    
    def decode(self, codeword: int) -> Tuple[int, str]:
        """Decode and correct approximate computing errors."""
        return self.approximate_ecc.decode(codeword)

FIR Multiplier ECC Integration:

# FIR Multiplier ECC integration
class FIRMultiplierECC(ECCBase):
    def __init__(self, word_length: int, filter_order: int):
        super().__init__()
        self.word_length = word_length
        self.filter_order = filter_order
        self.fir_ecc = self._optimize_for_fir()
    
    def _optimize_for_fir(self) -> ECCBase:
        """Optimize ECC for FIR filter characteristics."""
        # FIR filters have predictable error patterns
        if self.filter_order <= 8:
            return HammingSECDEDECC(self.word_length)
        elif self.filter_order <= 16:
            return BCHECC(self.word_length)
        else:
            return ReedSolomonECC(self.word_length)
    
    def encode(self, data: int) -> int:
        """Encode with FIR-optimized ECC."""
        return self.fir_ecc.encode(data)
    
    def decode(self, codeword: int) -> Tuple[int, str]:
        """Decode with FIR-optimized ECC."""
        return self.fir_ecc.decode(codeword)

Error Pattern Analysis for Approximate Multipliers:

# Error pattern analysis for approximate multipliers
def analyze_approximate_multiplier_errors():
    """Analyze error characteristics of approximate multipliers."""
    error_patterns = {
        'systematic': 0.6,    # 60% systematic errors
        'burst': 0.25,        # 25% burst errors
        'random': 0.15        # 15% random errors
    }
    
    # Select ECC based on error distribution
    if error_patterns['systematic'] > 0.5:
        recommended_ecc = "SystematicErrorECC"
    elif error_patterns['burst'] > 0.3:
        recommended_ecc = "BurstErrorECC"
    else:
        recommended_ecc = "LDPCECC"
    
    return recommended_ecc, error_patterns

Performance Evaluation for Approximate ECC:

# Performance evaluation for approximate multiplier ECC
def evaluate_approximate_ecc_performance():
    """Evaluate ECC performance with approximate multipliers."""
    metrics = {
        'error_correction_rate': 0.95,    # 95% error correction
        'overhead_ratio': 0.15,           # 15% overhead
        'latency_impact': 0.1,            # 10% latency increase
        'power_efficiency': 0.85          # 85% power efficiency
    }
    
    return metrics

Research Applications and Future Directions

Quantum-Resistant ECC

# Quantum-resistant ECC research
class QuantumResistantECC(ECCBase):
    def __init__(self, word_length: int, security_level: str = "128"):
        super().__init__()
        self.word_length = word_length
        self.security_level = security_level
        self.quantum_ecc = self._implement_quantum_resistant_ecc()
    
    def _implement_quantum_resistant_ecc(self) -> ECCBase:
        """Implement quantum-resistant ECC."""
        # Post-quantum cryptography integration
        if self.security_level == "128":
            return LatticeBasedECC(self.word_length)
        elif self.security_level == "256":
            return CodeBasedECC(self.word_length)
        else:
            return MultivariateECC(self.word_length)

Neuromorphic ECC

# Neuromorphic ECC for brain-inspired computing
class NeuromorphicECC(ECCBase):
    def __init__(self, word_length: int, neuron_count: int = 1000):
        super().__init__()
        self.word_length = word_length
        self.neuron_count = neuron_count
        self.neural_ecc = self._create_neural_ecc()
    
    def _create_neural_ecc(self) -> ECCBase:
        """Create brain-inspired ECC."""
        # Spiking neural network for error correction
        return SpikingNeuralECC(self.word_length, self.neuron_count)
    
    def encode(self, data: int) -> int:
        """Encode using neural-inspired ECC."""
        return self.neural_ecc.encode(data)
    
    def decode(self, codeword: int) -> Tuple[int, str]:
        """Decode using neural-inspired ECC."""
        return self.neural_ecc.decode(codeword)

AI-Optimized ECC

# AI-optimized ECC for machine learning workloads
class AIOptimizedECC(ECCBase):
    def __init__(self, word_length: int, ai_workload: str = "inference"):
        super().__init__()
        self.word_length = word_length
        self.ai_workload = ai_workload
        self.ai_ecc = self._optimize_for_ai()
    
    def _optimize_for_ai(self) -> ECCBase:
        """Optimize ECC for AI workloads."""
        if self.ai_workload == "training":
            return LDPCECC(self.word_length)  # High accuracy
        elif self.ai_workload == "inference":
            return HammingSECDEDECC(self.word_length)  # Fast
        else:
            return AdaptiveECC(self.word_length)  # Adaptive

Edge Computing ECC

# Edge computing ECC for IoT and edge devices
class EdgeComputingECC(ECCBase):
    def __init__(self, word_length: int, power_constraint: str = "low"):
        super().__init__()
        self.word_length = word_length
        self.power_constraint = power_constraint
        self.edge_ecc = self._optimize_for_edge()
    
    def _optimize_for_edge(self) -> ECCBase:
        """Optimize ECC for edge computing."""
        if self.power_constraint == "ultra_low":
            return ParityECC(self.word_length)
        elif self.power_constraint == "low":
            return HammingSECDEDECC(self.word_length)
        else:
            return BCHECC(self.word_length)

5G/6G Communication ECC

# 5G/6G communication ECC
class NextGenCommunicationECC(ECCBase):
    def __init__(self, word_length: int, generation: str = "5G"):
        super().__init__()
        self.word_length = word_length
        self.generation = generation
        self.comm_ecc = self._optimize_for_generation()
    
    def _optimize_for_generation(self) -> ECCBase:
        """Optimize ECC for 5G/6G communications."""
        if self.generation == "5G":
            return PolarECC(self.word_length)
        elif self.generation == "6G":
            return AdvancedPolarECC(self.word_length)
        else:
            return TurboECC(self.word_length)

Comprehensive Shell Script Examples

The run_all.sh script provides a unified interface for all framework operations:

# Basic operations
./run_all.sh                                    # Full analysis (default)
./run_all.sh -m theoretical                     # Theoretical analysis only
./run_all.sh -m hardware                        # Hardware analysis only
./run_all.sh -m hardware -s                     # Hardware without report

# Parallel processing
./run_all.sh --use-processes --workers 8        # Multiprocessing with 8 workers
./run_all.sh --chunked --workers 4              # Chunked processing
./run_all.sh -p auto                            # Auto-detect optimal settings
./run_all.sh -m benchmark --use-processes       # Benchmark with multiprocessing

# Performance testing
./run_all.sh --quick-test                       # Quick framework validation
./run_all.sh --concurrent-demo                  # Visual concurrent execution demo
./run_all.sh -m performance                     # Comprehensive performance analysis

# Analysis and reporting
./run_all.sh -m analysis                        # Generate report from existing data
./run_all.sh -m benchmark                       # Run benchmarks only

# Design exploration
./run_all.sh -m design-exploration              # Explore ECC combinations

# Verbose and debugging
./run_all.sh -v -m full                         # Verbose full analysis
./run_all.sh --help                             # Show all options

Custom ECC Implementation

To add a new ECC type:

Create a new ECC class inheriting from ECCBase:

from .base_ecc import ECCBase
from typing import Tuple

class MyCustomECC(ECCBase):
    def encode(self, data: int) -> int:
        # Implementation
        pass
    
    def decode(self, codeword: int) -> Tuple[int, bool, bool]:
        # Implementation
        pass

Add to configuration:

{
  "ecc_types": ["MyCustomECC"]
}

Custom Analysis

Extend the analysis framework:

from enhanced_analysis import ECCAnalyzer

analyzer = ECCAnalyzer(benchmark_results)
custom_analysis = analyzer.analyze_performance_rankings()

Batch Processing

Process multiple configurations:

# Test different word lengths
for length in 8 16 32; do
    python run_analysis.py --config config_${length}.json --output results_${length}
done

Use Cases by Mode

Theoretical Mode

Algorithm development and testing
Performance comparison of different ECC codes
Educational purposes
Quick validation of ECC parameters

Hardware Mode

Hardware implementation verification
Synthesis optimization
Area and timing analysis
FPGA/ASIC design validation

Full Mode

Complete ECC framework validation
Research publications
Comprehensive performance analysis
Documentation generation

Performance Mode

Performance optimization
Scalability testing
Parallel processing evaluation
System resource analysis

Quick Test Mode

Framework validation
Quick functionality verification
Development testing
CI/CD integration

Concurrent Demo Mode

Educational demonstrations
Parallel processing visualization
Framework showcase
Training and tutorials

Design Exploration Mode

Primary/secondary ECC combinations
Design space exploration
Multi-level ECC analysis
Advanced ECC architectures

Performance Considerations

Theoretical mode: Fastest execution, suitable for algorithm development
Hardware mode: Moderate execution time, includes synthesis and simulation
Full mode: Longest execution time, comprehensive analysis
Parallel processing: Significantly faster execution for large datasets
Quick test mode: Very fast execution for framework validation
Concurrent demo mode: Fast execution with visual feedback
Design exploration mode: Variable execution time based on exploration scope

Troubleshooting

Common Issues

Import Errors
- Ensure you're running from the src directory
- Check that all ECC implementation files exist
Missing Hardware Tools
- Framework will continue without hardware verification
- Reports will indicate missing hardware data
Memory Issues
- Reduce trials_per_config for large configurations
- Use fewer max_workers for limited memory
- Use --chunked option for memory management
Long Execution Times
- Reduce number of ECC types or word lengths
- Increase max_workers for faster execution
- Use --use-processes for CPU-intensive workloads
Shell Script Issues
- Ensure run_all.sh has execute permissions: chmod +x run_all.sh
- Check that virtual environment is properly set up
- Verify all Python dependencies are installed

Verilator Not Found

If you see warnings about Verilator simulation being skipped:

# Install Verilator (Ubuntu/Debian)
sudo apt-get install verilator

# Install Verilator (macOS)
brew install verilator

# Install Verilator (Windows with WSL)
sudo apt-get install verilator

Yosys Not Found

If synthesis fails:

# Install Yosys (Ubuntu/Debian)
sudo apt-get install yosys

# Install Yosys (macOS)
brew install yosys

Debug Mode

Enable verbose output:

# Python script verbose mode
python run_analysis.py --debug

# Shell script verbose mode
./run_all.sh -v -m full

Contributing

Adding New ECC Types

Implement the ECC class following the ECCBase interface
Add appropriate parameters in benchmark_suite.py
Update documentation and examples

Extending Analysis

Add new analysis methods to ECCAnalyzer
Update visualization functions
Extend report generation

Improving Hardware Support

Add new synthesis tool integrations
Extend testbench validation
Improve error handling and reporting

Results Interpretation

Simulation and benchmark results are saved in results/ as Markdown and plots.
Compare error correction/detection rates and hardware cost for each ECC.
Use the Markdown tables for publication or reporting.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.

This framework is provided for educational and research purposes. Please ensure compliance with any applicable licenses for included ECC implementations and tools.

Support

For issues and questions:

Check the troubleshooting section
Review the example configurations
Examine the source code documentation
Run with --debug for detailed error information

Conclusion

This comprehensive ECC Analysis Framework provides a complete solution for error correction code evaluation, optimization, and implementation. The framework supports:

Key Capabilities

25+ ECC Types: From basic parity to advanced quantum-resistant codes
Advanced Analysis: Statistical analysis, performance optimization, and ML integration
Design Space Exploration: Multi-level ECC architectures and adaptive systems
Hardware Verification: Synthesis and testbench validation
Parallel Processing: High-performance benchmarking and analysis
Research Applications: Quantum-resistant, neuromorphic, and AI-optimized ECC

Application Domains

Memory Systems: DDR/HBM ECC optimization
Communication: 5G/6G and wireless systems
Storage: High-reliability data protection
Embedded Systems: IoT and edge computing
AI/ML: Approximate computing and neural networks
Aerospace: High-reliability applications

Future Directions

Quantum Computing: Post-quantum cryptography integration
Neuromorphic Computing: Brain-inspired error correction
Edge AI: Optimized ECC for edge devices
6G Communications: Next-generation wireless ECC
Advanced Memory: 3D and emerging memory technologies

Getting Started

# Quick start
./run_all.sh

# Advanced analysis
./run_all.sh -m theoretical --use-processes --workers 8

# Design exploration
./run_all.sh -m design-exploration

# Performance testing
./run_all.sh --performance-test

The framework is designed to be extensible, allowing researchers and engineers to add new ECC types, analysis methods, and optimization strategies. Whether you're developing new error correction codes, optimizing existing implementations, or exploring novel applications, this framework provides the tools and infrastructure needed for comprehensive ECC analysis and development.

Name		Name	Last commit message	Last commit date
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docs		docs
results		results
src		src
testbenches		testbenches
verilogs		verilogs
LICENSE		LICENSE
README.md		README.md
example_config.json		example_config.json
requirements.txt		requirements.txt
run_all.sh		run_all.sh

License

SJTU-YONGFU-RESEARCH-GRP/core-cc

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ECC Analysis Framework

Table of Contents

Features

🚀 Comprehensive Benchmarking

📋 Supported ECC Types

Basic ECC Codes

Advanced ECC Codes

Modern ECC Codes

Advanced ECC Codes

Specialized ECC Codes

ECC Characteristics Comparison

Error Pattern Handling

Performance Trade-offs

Application Recommendations

Memory Technology ECC Comparison

DDR Generations ECC Evolution

HBM (High Bandwidth Memory) ECC Characteristics

HBM vs DDR ECC Architecture

Future Memory ECC Trends

📊 Advanced Analysis

🔧 Hardware Verification

📈 Enhanced Reporting

⚡ Parallel Processing

Quick Start

1. Basic Usage

2. Python Framework Usage

3. Custom Configuration

4. Selective Execution

5. Parallel Processing Options

6. Performance Testing and Demonstration

7. Shell Script Integration

Setup

Linux/macOS

Windows (with WSL)

Hardware Requirements

Optional Tools

Installation

Usage Guide

Execution Modes

Command Line Options

Shell Script Options (run_all.sh)

Python Script Options (run_analysis.py)

Configuration Options

Benchmark Configuration

Error Patterns

Output Files

Benchmark Results

Analysis Visualizations

Hardware Verification

Final Report

Logs

Individual Testbench Execution

Execution Plan and Logging

Performance Metrics

Primary Metrics

Timing Metrics

Hardware Metrics

Framework Architecture

Core Modules

1. Benchmark Suite (benchmark_suite.py)

2. Enhanced Analysis (enhanced_analysis.py)

3. Parallel Processing (run_analysis.py)

4. Hardware Verification (hardware_verification.py)

5. Report Generator (report_generator.py)

6. Main Orchestrator (run_analysis.py)

Data Flow

Advanced Usage

Advanced ECC Analysis

Statistical Analysis and Performance Evaluation

Error Pattern Analysis

Performance Optimization Strategies

Machine Learning Integration

Design Space Exploration

Primary-Secondary ECC Combinations

Shell Script Options (`run_all.sh`)

Python Script Options (`run_analysis.py`)

1. Benchmark Suite (`benchmark_suite.py`)

2. Enhanced Analysis (`enhanced_analysis.py`)

3. Parallel Processing (`run_analysis.py`)

4. Hardware Verification (`hardware_verification.py`)

5. Report Generator (`report_generator.py`)

6. Main Orchestrator (`run_analysis.py`)

Packages