optirs-learned 0.3.1

use std::fmt::Debug;
// Hardware-aware evaluation for neural architecture search
//
// This module provides evaluation methods that consider hardware-specific
// constraints such as latency, memory usage, energy consumption, and throughput.

#[allow(dead_code)]

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use std::collections::HashMap;

use crate::error::{OptimError, Result};

/// Configuration for hardware-aware evaluation
#[derive(Debug, Clone)]
pub struct HardwareAwareConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Target hardware platforms
    pub target_platforms: Vec<HardwarePlatform>,
    
    /// Hardware constraints
    pub constraints: HardwareConstraints<T>,
    
    /// Performance models
    pub performance_models: HashMap<String, PerformanceModel<T>>,
    
    /// Measurement configuration
    pub measurement_config: MeasurementConfig,
    
    /// Optimization objectives
    pub optimization_objectives: Vec<HardwareObjective<T>>,
    
    /// Energy budget constraints
    pub energy_budget: Option<EnergyBudget<T>>,
    
    /// Thermal constraints
    pub thermal_constraints: Option<ThermalConstraints<T>>,
}

/// Hardware platform specifications
#[derive(Debug, Clone)]
pub struct HardwarePlatform {
    /// Platform identifier
    pub id: String,
    
    /// Platform name
    pub name: String,
    
    /// Hardware type
    pub hardware_type: HardwareType,
    
    /// Compute capabilities
    pub compute_capabilities: ComputeCapabilities,
    
    /// Memory specifications
    pub memory_specs: MemorySpecs,
    
    /// Power specifications
    pub power_specs: PowerSpecs,
    
    /// Hardware-specific optimizations
    pub optimizations: Vec<HardwareOptimization>,
}

/// Types of hardware platforms
#[derive(Debug, Clone, Copy)]
pub enum HardwareType {
    /// CPU-based platforms
    CPU { cores: usize, frequency_ghz: f64 },
    
    /// GPU-based platforms
    GPU { cuda_cores: usize, memory_gb: f64 },
    
    /// TPU-based platforms
    TPU { version: String, cores: usize },
    
    /// Edge devices
    Edge { chip: String, power_watts: f64 },
    
    /// Mobile devices
    Mobile { soc: String, battery_mah: f64 },
    
    /// FPGA platforms
    FPGA { logic_elements: usize, memory_gb: f64 },
    
    /// Custom accelerators
    Custom { accelerator_type: String, specs: HashMap<String, f64> },
}

/// Compute capabilities of hardware
#[derive(Debug, Clone)]
pub struct ComputeCapabilities {
    /// Peak FLOPS (floating-point operations per second)
    pub peak_flops: u64,
    
    /// Memory bandwidth (GB/s)
    pub memory_bandwidth: f64,
    
    /// Cache sizes (bytes)
    pub cache_sizes: Vec<usize>,
    
    /// Instruction set support
    pub instruction_sets: Vec<String>,
    
    /// Parallel execution units
    pub execution_units: usize,
    
    /// Clock frequency (Hz)
    pub clock_frequency: f64,
}

/// Memory specifications
#[derive(Debug, Clone)]
pub struct MemorySpecs {
    /// Total memory size (bytes)
    pub total_memory: usize,
    
    /// Available memory (bytes)
    pub available_memory: usize,
    
    /// Memory types (DDR4, GDDR6, HBM, etc.)
    pub memory_types: Vec<String>,
    
    /// Memory hierarchy latencies (cycles)
    pub access_latencies: HashMap<String, u32>,
    
    /// Memory bandwidth per type (GB/s)
    pub bandwidth_per_type: HashMap<String, f64>,
}

/// Power specifications
#[derive(Debug, Clone)]
pub struct PowerSpecs {
    /// Maximum power consumption (watts)
    pub max_power: f64,
    
    /// Idle power consumption (watts)
    pub idle_power: f64,
    
    /// Power efficiency (FLOPS/watt)
    pub efficiency: f64,
    
    /// Thermal design power (watts)
    pub tdp: f64,
    
    /// Power states and transitions
    pub power_states: Vec<PowerState>,
}

/// Power states for hardware
#[derive(Debug, Clone)]
pub struct PowerState {
    /// State name
    pub name: String,
    
    /// Power consumption (watts)
    pub power_consumption: f64,
    
    /// Performance scaling factor
    pub performance_scale: f64,
    
    /// Transition latency (microseconds)
    pub transition_latency: f64,
}

/// Hardware-specific optimizations
#[derive(Debug, Clone)]
pub enum HardwareOptimization {
    /// Instruction-level optimization
    InstructionLevel(String),
    
    /// Memory access optimization
    MemoryAccess(String),
    
    /// Parallel processing optimization
    Parallelization(String),
    
    /// Cache optimization
    Cache(String),
    
    /// Compiler optimization
    Compiler(String),
}

/// Hardware constraints
#[derive(Debug, Clone)]
pub struct HardwareConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum latency (milliseconds)
    pub max_latency: Option<T>,
    
    /// Maximum memory usage (bytes)
    pub max_memory: Option<usize>,
    
    /// Maximum power consumption (watts)
    pub max_power: Option<T>,
    
    /// Maximum energy per inference (joules)
    pub max_energy_per_inference: Option<T>,
    
    /// Minimum throughput (inferences/second)
    pub min_throughput: Option<T>,
    
    /// Maximum model size (bytes)
    pub max_model_size: Option<usize>,
    
    /// Maximum temperature (Celsius)
    pub max_temperature: Option<T>,
}

/// Performance models for hardware prediction
#[derive(Debug, Clone)]
pub struct PerformanceModel<T: Float + Debug + Send + Sync + 'static> {
    /// Model identifier
    pub id: String,
    
    /// Model type
    pub model_type: ModelType,
    
    /// Model parameters
    pub parameters: Array1<T>,
    
    /// Feature extractors
    pub feature_extractors: Vec<FeatureExtractor>,
    
    /// Model accuracy metrics
    pub accuracy_metrics: ModelAccuracyMetrics<T>,
    
    /// Supported operations
    pub supported_operations: Vec<String>,
}

/// Types of performance models
#[derive(Debug, Clone)]
pub enum ModelType {
    /// Linear regression model
    LinearRegression,
    
    /// Polynomial regression model
    PolynomialRegression { degree: usize },
    
    /// Neural network model
    NeuralNetwork { layers: Vec<usize> },
    
    /// Lookup table model
    LookupTable,
    
    /// Analytical model
    Analytical,
    
    /// Profiling-based model
    ProfilingBased,
}

/// Feature extractors for performance models
#[derive(Debug, Clone)]
pub struct FeatureExtractor {
    /// Feature name
    pub name: String,
    
    /// Extraction method
    pub method: ExtractionMethod,
    
    /// Feature importance weight
    pub importance: f64,
}

/// Methods for feature extraction
#[derive(Debug, Clone)]
pub enum ExtractionMethod {
    /// Architecture-based features
    Architecture(ArchitectureFeatures),
    
    /// Operation-based features
    Operation(OperationFeatures),
    
    /// Graph-based features
    Graph(GraphFeatures),
    
    /// Statistical features
    Statistical(StatisticalFeatures),
}

/// Architecture-based features
#[derive(Debug, Clone)]
pub struct ArchitectureFeatures {
    /// Number of layers
    pub num_layers: bool,
    
    /// Layer types distribution
    pub layer_types: bool,
    
    /// Parameter count
    pub parameter_count: bool,
    
    /// Model depth
    pub depth: bool,
    
    /// Skip connections
    pub skip_connections: bool,
}

/// Operation-based features
#[derive(Debug, Clone)]
pub struct OperationFeatures {
    /// FLOPS count
    pub flops: bool,
    
    /// Memory access patterns
    pub memory_access: bool,
    
    /// Computation intensity
    pub compute_intensity: bool,
    
    /// Data dependencies
    pub dependencies: bool,
}

/// Graph-based features
#[derive(Debug, Clone)]
pub struct GraphFeatures {
    /// Graph connectivity
    pub connectivity: bool,
    
    /// Path lengths
    pub path_lengths: bool,
    
    /// Branching factor
    pub branching_factor: bool,
    
    /// Graph density
    pub density: bool,
}

/// Statistical features
#[derive(Debug, Clone)]
pub struct StatisticalFeatures {
    /// Mean activation sizes
    pub activation_stats: bool,
    
    /// Weight distributions
    pub weight_stats: bool,
    
    /// Gradient statistics
    pub gradient_stats: bool,
}

/// Model accuracy metrics
#[derive(Debug, Clone)]
pub struct ModelAccuracyMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Mean absolute error
    pub mae: T,
    
    /// Root mean square error
    pub rmse: T,
    
    /// R-squared value
    pub r_squared: T,
    
    /// Mean absolute percentage error
    pub mape: T,
    
    /// Prediction interval coverage
    pub coverage: T,
}

/// Measurement configuration
#[derive(Debug, Clone)]
pub struct MeasurementConfig {
    /// Number of warmup runs
    pub warmup_runs: usize,
    
    /// Number of measurement runs
    pub measurement_runs: usize,
    
    /// Measurement precision level
    pub precision_level: PrecisionLevel,
    
    /// Batch sizes for measurement
    pub batch_sizes: Vec<usize>,
    
    /// Input shapes for measurement
    pub input_shapes: Vec<Vec<usize>>,
    
    /// Measurement timeout (seconds)
    pub timeout_seconds: u32,
}

/// Precision levels for measurement
#[derive(Debug, Clone, Copy)]
pub enum PrecisionLevel {
    /// Quick approximate measurement
    Quick,
    
    /// Standard measurement
    Standard,
    
    /// High precision measurement
    HighPrecision,
    
    /// Exhaustive measurement
    Exhaustive,
}

/// Hardware optimization objectives
#[derive(Debug, Clone)]
pub struct HardwareObjective<T: Float + Debug + Send + Sync + 'static> {
    /// Objective identifier
    pub id: String,
    
    /// Objective type
    pub objective_type: HardwareObjectiveType,
    
    /// Target value
    pub target_value: Option<T>,
    
    /// Weight in multi-objective optimization
    pub weight: T,
    
    /// Constraint threshold
    pub constraint_threshold: Option<T>,
}

/// Types of hardware objectives
#[derive(Debug, Clone, Copy)]
pub enum HardwareObjectiveType {
    /// Minimize inference latency
    MinimizeLatency,
    
    /// Minimize memory usage
    MinimizeMemory,
    
    /// Minimize power consumption
    MinimizePower,
    
    /// Minimize energy per inference
    MinimizeEnergy,
    
    /// Maximize throughput
    MaximizeThroughput,
    
    /// Maximize efficiency (performance/power)
    MaximizeEfficiency,
    
    /// Minimize temperature
    MinimizeTemperature,
    
    /// Maximize utilization
    MaximizeUtilization,
}

/// Energy budget constraints
#[derive(Debug, Clone)]
pub struct EnergyBudget<T: Float + Debug + Send + Sync + 'static> {
    /// Total energy budget (joules)
    pub total_budget: T,
    
    /// Energy per inference limit (joules)
    pub per_inference_limit: T,
    
    /// Battery capacity (watt-hours)
    pub battery_capacity: Option<T>,
    
    /// Energy efficiency target (inferences/joule)
    pub efficiency_target: T,
}

/// Thermal constraints
#[derive(Debug, Clone)]
pub struct ThermalConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum operating temperature (Celsius)
    pub max_temperature: T,
    
    /// Thermal throttling threshold (Celsius)
    pub throttling_threshold: T,
    
    /// Cooling capacity (watts)
    pub cooling_capacity: T,
    
    /// Thermal time constants (seconds)
    pub thermal_time_constants: Vec<T>,
}

/// Hardware-aware evaluator
#[derive(Debug)]
pub struct HardwareAwareEvaluator<T: Float + Debug + Send + Sync + 'static> {
    /// Configuration
    config: HardwareAwareConfig<T>,
    
    /// Performance measurements cache
    measurement_cache: HashMap<String, PerformanceMeasurement<T>>,
    
    /// Hardware profiling data
    profiling_data: HashMap<String, ProfilingData<T>>,
    
    /// Evaluation history
    evaluation_history: Vec<HardwareEvaluation<T>>,
    
    /// Current platform
    current_platform: Option<HardwarePlatform>,
}

/// Performance measurement result
#[derive(Debug, Clone)]
pub struct PerformanceMeasurement<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture identifier
    pub architecture_id: String,
    
    /// Target platform
    pub platform_id: String,
    
    /// Latency measurements (milliseconds)
    pub latency_ms: Vec<T>,
    
    /// Memory usage (bytes)
    pub memory_usage: usize,
    
    /// Power consumption (watts)
    pub power_consumption: T,
    
    /// Energy per inference (joules)
    pub energy_per_inference: T,
    
    /// Throughput (inferences/second)
    pub throughput: T,
    
    /// Temperature measurements (Celsius)
    pub temperatures: Vec<T>,
    
    /// Utilization percentages
    pub utilizations: HashMap<String, T>,
    
    /// Measurement timestamp
    pub timestamp: u64,
    
    /// Measurement quality metrics
    pub quality_metrics: MeasurementQuality<T>,
}

/// Profiling data for hardware
#[derive(Debug, Clone)]
pub struct ProfilingData<T: Float + Debug + Send + Sync + 'static> {
    /// Operation-level timings
    pub operation_timings: HashMap<String, T>,
    
    /// Memory access patterns
    pub memory_patterns: MemoryAccessPattern<T>,
    
    /// Cache hit rates
    pub cache_stats: HashMap<String, T>,
    
    /// Power breakdown by component
    pub power_breakdown: HashMap<String, T>,
    
    /// Thermal profile
    pub thermal_profile: ThermalProfile<T>,
}

/// Memory access patterns
#[derive(Debug, Clone)]
pub struct MemoryAccessPattern<T: Float + Debug + Send + Sync + 'static> {
    /// Sequential access ratio
    pub sequential_ratio: T,
    
    /// Cache locality score
    pub locality_score: T,
    
    /// Memory bandwidth utilization
    pub bandwidth_utilization: T,
    
    /// Access stride patterns
    pub stride_patterns: Vec<usize>,
}

/// Thermal profile
#[derive(Debug, Clone)]
pub struct ThermalProfile<T: Float + Debug + Send + Sync + 'static> {
    /// Temperature over time
    pub temperature_timeline: Vec<(T, T)>, // (time, temperature)
    
    /// Hot spots identification
    pub hot_spots: Vec<String>,
    
    /// Thermal gradients
    pub thermal_gradients: HashMap<String, T>,
    
    /// Cooling effectiveness
    pub cooling_effectiveness: T,
}

/// Measurement quality metrics
#[derive(Debug, Clone)]
pub struct MeasurementQuality<T: Float + Debug + Send + Sync + 'static> {
    /// Measurement variance
    pub variance: T,
    
    /// Confidence interval
    pub confidence_interval: (T, T),
    
    /// Number of outliers removed
    pub outliers_removed: usize,
    
    /// Signal-to-noise ratio
    pub signal_noise_ratio: T,
}

/// Hardware evaluation result
#[derive(Debug, Clone)]
pub struct HardwareEvaluation<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture identifier
    pub architecture_id: String,
    
    /// Evaluation results per platform
    pub platform_results: HashMap<String, PlatformEvaluationResult<T>>,
    
    /// Overall hardware score
    pub overall_score: T,
    
    /// Constraint violations
    pub constraint_violations: Vec<ConstraintViolation<T>>,
    
    /// Optimization recommendations
    pub recommendations: Vec<OptimizationRecommendation>,
    
    /// Evaluation timestamp
    pub timestamp: u64,
}

/// Evaluation result for a specific platform
#[derive(Debug, Clone)]
pub struct PlatformEvaluationResult<T: Float + Debug + Send + Sync + 'static> {
    /// Platform identifier
    pub platform_id: String,
    
    /// Performance measurements
    pub measurements: PerformanceMeasurement<T>,
    
    /// Predicted vs. actual performance
    pub prediction_accuracy: T,
    
    /// Resource utilization efficiency
    pub utilization_efficiency: T,
    
    /// Hardware-specific score
    pub platform_score: T,
    
    /// Bottleneck analysis
    pub bottlenecks: Vec<Bottleneck>,
}

/// Constraint violation
#[derive(Debug, Clone)]
pub struct ConstraintViolation<T: Float + Debug + Send + Sync + 'static> {
    /// Constraint type
    pub constraint_type: String,
    
    /// Expected value
    pub expected: T,
    
    /// Actual value
    pub actual: T,
    
    /// Violation severity
    pub severity: ViolationSeverity,
    
    /// Suggested fixes
    pub suggested_fixes: Vec<String>,
}

/// Severity of constraint violations
#[derive(Debug, Clone, Copy)]
pub enum ViolationSeverity {
    /// Warning level violation
    Warning,
    
    /// Error level violation
    Error,
    
    /// Critical violation
    Critical,
}

/// Optimization recommendations
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    
    /// Description
    pub description: String,
    
    /// Expected improvement
    pub expected_improvement: HashMap<String, f64>,
    
    /// Implementation difficulty
    pub difficulty: DifficultyLevel,
    
    /// Priority level
    pub priority: u8,
}

/// Types of optimization recommendations
#[derive(Debug, Clone)]
pub enum RecommendationType {
    /// Architecture modification
    ArchitectureModification,
    
    /// Operation fusion
    OperationFusion,
    
    /// Memory optimization
    MemoryOptimization,
    
    /// Quantization
    Quantization,
    
    /// Pruning
    Pruning,
    
    /// Hardware-specific optimization
    HardwareSpecific(String),
}

/// Implementation difficulty levels
#[derive(Debug, Clone, Copy)]
pub enum DifficultyLevel {
    Easy,
    Medium,
    Hard,
    Expert,
}

/// System bottlenecks
#[derive(Debug, Clone)]
pub struct Bottleneck {
    /// Bottleneck type
    pub bottleneck_type: BottleneckType,
    
    /// Severity (0.0 to 1.0)
    pub severity: f64,
    
    /// Description
    pub description: String,
    
    /// Affected operations
    pub affected_operations: Vec<String>,
}

/// Types of system bottlenecks
#[derive(Debug, Clone)]
pub enum BottleneckType {
    /// Compute-bound bottleneck
    Compute,
    
    /// Memory bandwidth bottleneck
    MemoryBandwidth,
    
    /// Memory capacity bottleneck
    MemoryCapacity,
    
    /// I/O bottleneck
    IO,
    
    /// Thermal throttling
    Thermal,
    
    /// Power limit
    Power,
    
    /// Cache miss bottleneck
    Cache,
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> HardwareAwareEvaluator<T> {
    /// Create new hardware-aware evaluator
    pub fn new(config: HardwareAwareConfig<T>) -> Self {
        Self {
            config,
            measurement_cache: HashMap::new(),
            profiling_data: HashMap::new(),
            evaluation_history: Vec::new(),
            current_platform: None,
        }
    }
    
    /// Set target platform for evaluation
    pub fn set_target_platform(&mut self, platform: HardwarePlatform) {
        self.current_platform = Some(platform);
    }
    
    /// Evaluate architecture on hardware platforms
    pub fn evaluate(&mut self, architecture_id: &str, architecture_data: &HashMap<String, f64>) -> Result<HardwareEvaluation<T>> {
        let mut platform_results = HashMap::new();
        let mut constraint_violations = Vec::new();
        let mut overall_score = T::zero();
        
        // Evaluate on each target platform
        for platform in &self.config.target_platforms {
            let result = self.evaluate_on_platform(architecture_id, architecture_data, platform)?;
            
            // Check constraints for this platform
            let violations = self.check_constraints(&result.measurements, platform)?;
            constraint_violations.extend(violations);
            
            // Update overall score
            overall_score = overall_score + result.platform_score;
            
            platform_results.insert(platform.id.clone(), result);
        }
        
        // Normalize overall score
        if !self.config.target_platforms.is_empty() {
            overall_score = overall_score / T::from(self.config.target_platforms.len() as f64).expect("unwrap failed");
        }
        
        // Generate optimization recommendations
        let recommendations = self.generate_recommendations(architecture_id, &platform_results)?;
        
        let evaluation = HardwareEvaluation {
            architecture_id: architecture_id.to_string(),
            platform_results,
            overall_score,
            constraint_violations,
            recommendations,
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap_or_default()
                .as_secs(),
        };
        
        self.evaluation_history.push(evaluation.clone());
        
        Ok(evaluation)
    }
    
    /// Evaluate architecture on a specific platform
    fn evaluate_on_platform(&mut self, architecture_id: &str, 
                           architecture_data: &HashMap<String, f64>,
                           platform: &HardwarePlatform) -> Result<PlatformEvaluationResult<T>> {
        // Check cache for existing measurements
        let cache_key = format!("{}_{}", architecture_id, platform.id);
        if let Some(cached_measurement) = self.measurement_cache.get(&cache_key) {
            return Ok(PlatformEvaluationResult {
                platform_id: platform.id.clone(),
                measurements: cached_measurement.clone(),
                prediction_accuracy: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()), // Cached, assume good accuracy
                utilization_efficiency: self.compute_utilization_efficiency(cached_measurement, platform)?,
                platform_score: self.compute_platform_score(cached_measurement, platform)?,
                bottlenecks: self.identify_bottlenecks(cached_measurement, platform)?,
            });
        }
        
        // Perform actual measurement
        let measurement = self.measure_performance(architecture_id, architecture_data, platform)?;
        
        // Cache the measurement
        self.measurement_cache.insert(cache_key, measurement.clone());
        
        // Compute platform-specific metrics
        let prediction_accuracy = self.validate_prediction(architecture_data, &measurement, platform)?;
        let utilization_efficiency = self.compute_utilization_efficiency(&measurement, platform)?;
        let platform_score = self.compute_platform_score(&measurement, platform)?;
        let bottlenecks = self.identify_bottlenecks(&measurement, platform)?;
        
        Ok(PlatformEvaluationResult {
            platform_id: platform.id.clone(),
            measurements: measurement,
            prediction_accuracy,
            utilization_efficiency,
            platform_score,
            bottlenecks,
        })
    }
    
    /// Measure performance on hardware platform
    fn measure_performance(&self, architecture_id: &str,
                          architecture_data: &HashMap<String, f64>,
                          platform: &HardwarePlatform) -> Result<PerformanceMeasurement<T>> {
        // Simulate performance measurement
        // In practice, this would run actual benchmarks on the hardware
        
        let base_latency = architecture_data.get("estimated_latency").unwrap_or(&50.0);
        let base_memory = architecture_data.get("estimated_memory").unwrap_or(&1024.0) as usize;
        let base_power = architecture_data.get("estimated_power").unwrap_or(&100.0);
        
        // Apply platform-specific scaling factors
        let platform_factor = match platform.hardware_type {
            HardwareType::GPU { .. } => 0.3, // GPUs are typically faster
            HardwareType::TPU { .. } => 0.2, // TPUs are even faster for ML workloads
            HardwareType::Edge { .. } => 2.0, // Edge devices are slower
            HardwareType::Mobile { .. } => 3.0, // Mobile devices are much slower
            _ => 1.0,
        };
        
        // Generate latency measurements with some variance
        let mut latency_measurements = Vec::new();
        for _ in 0..self.config.measurement_config.measurement_runs {
            let variance = 1.0 + (scirs2_core::random::random::<f64>() - 0.5) * 0.2; // ±10% variance
            let latency = scirs2_core::numeric::NumCast::from(base_latency * platform_factor * variance).unwrap_or_else(|| T::zero());
            latency_measurements.push(latency);
        }
        
        // Compute derived metrics
        let avg_latency = latency_measurements.iter().cloned().fold(T::zero(), |acc, x| acc + x) /
                         T::from(latency_measurements.len() as f64).expect("unwrap failed");
        let throughput = scirs2_core::numeric::NumCast::from(1000.0).unwrap_or_else(|| T::zero()) / avg_latency; // inferences per second
        let power_consumption = scirs2_core::numeric::NumCast::from(base_power * platform_factor).unwrap_or_else(|| T::zero());
        let energy_per_inference = power_consumption * avg_latency / scirs2_core::numeric::NumCast::from(1000.0).unwrap_or_else(|| T::zero());
        
        // Generate temperature measurements
        let base_temp = 40.0 + base_power * platform_factor * 0.3;
        let temperatures = vec![
            scirs2_core::numeric::NumCast::from(base_temp).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(base_temp + 5.0).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(base_temp + 2.0).unwrap_or_else(|| T::zero()),
        ];
        
        // Generate utilization data
        let mut utilizations = HashMap::new();
        utilizations.insert("compute".to_string(), scirs2_core::numeric::NumCast::from(0.75).unwrap_or_else(|| T::zero()));
        utilizations.insert("memory".to_string(), scirs2_core::numeric::NumCast::from(0.60).unwrap_or_else(|| T::zero()));
        utilizations.insert("bandwidth".to_string(), scirs2_core::numeric::NumCast::from(0.45).unwrap_or_else(|| T::zero()));
        
        // Compute quality metrics
        let variance = latency_measurements.iter()
            .map(|&x| (x - avg_latency) * (x - avg_latency))
            .fold(T::zero(), |acc, x| acc + x) / T::from(latency_measurements.len() as f64).expect("unwrap failed");
        
        let quality_metrics = MeasurementQuality {
            variance,
            confidence_interval: (avg_latency * scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()), avg_latency * scirs2_core::numeric::NumCast::from(1.05).unwrap_or_else(|| T::zero())),
            outliers_removed: 0,
            signal_noise_ratio: scirs2_core::numeric::NumCast::from(20.0).unwrap_or_else(|| T::zero()),
        };
        
        Ok(PerformanceMeasurement {
            architecture_id: architecture_id.to_string(),
            platform_id: platform.id.clone(),
            latency_ms: latency_measurements,
            memory_usage: (base_memory as f64 * platform_factor) as usize,
            power_consumption,
            energy_per_inference,
            throughput,
            temperatures,
            utilizations,
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap_or_default()
                .as_secs(),
            quality_metrics,
        })
    }
    
    /// Check hardware constraints
    fn check_constraints(&self, measurement: &PerformanceMeasurement<T>, 
                        platform: &HardwarePlatform) -> Result<Vec<ConstraintViolation<T>>> {
        let mut violations = Vec::new();
        let constraints = &self.config.constraints;
        
        // Check latency constraint
        if let Some(max_latency) = constraints.max_latency {
            let avg_latency = measurement.latency_ms.iter().cloned().fold(T::zero(), |acc, x| acc + x) /
                             T::from(measurement.latency_ms.len() as f64).expect("unwrap failed");
            if avg_latency > max_latency {
                violations.push(ConstraintViolation {
                    constraint_type: "max_latency".to_string(),
                    expected: max_latency,
                    actual: avg_latency,
                    severity: if avg_latency > max_latency * scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero()) {
                        ViolationSeverity::Critical
                    } else {
                        ViolationSeverity::Error
                    },
                    suggested_fixes: vec![
                        "Reduce model complexity".to_string(),
                        "Use model quantization".to_string(),
                        "Optimize operations".to_string(),
                    ],
                });
            }
        }
        
        // Check memory constraint
        if let Some(max_memory) = constraints.max_memory {
            if measurement.memory_usage > max_memory {
                violations.push(ConstraintViolation {
                    constraint_type: "max_memory".to_string(),
                    expected: scirs2_core::numeric::NumCast::from(max_memory as f64).unwrap_or_else(|| T::zero()),
                    actual: scirs2_core::numeric::NumCast::from(measurement.memory_usage as f64).unwrap_or_else(|| T::zero()),
                    severity: ViolationSeverity::Error,
                    suggested_fixes: vec![
                        "Reduce batch size".to_string(),
                        "Use gradient checkpointing".to_string(),
                        "Apply model pruning".to_string(),
                    ],
                });
            }
        }
        
        // Check power constraint
        if let Some(max_power) = constraints.max_power {
            if measurement.power_consumption > max_power {
                violations.push(ConstraintViolation {
                    constraint_type: "max_power".to_string(),
                    expected: max_power,
                    actual: measurement.power_consumption,
                    severity: ViolationSeverity::Warning,
                    suggested_fixes: vec![
                        "Reduce clock frequency".to_string(),
                        "Use power-efficient operations".to_string(),
                        "Enable power saving modes".to_string(),
                    ],
                });
            }
        }
        
        // Check throughput constraint
        if let Some(min_throughput) = constraints.min_throughput {
            if measurement.throughput < min_throughput {
                violations.push(ConstraintViolation {
                    constraint_type: "min_throughput".to_string(),
                    expected: min_throughput,
                    actual: measurement.throughput,
                    severity: ViolationSeverity::Error,
                    suggested_fixes: vec![
                        "Increase parallelism".to_string(),
                        "Optimize memory access".to_string(),
                        "Use batch processing".to_string(),
                    ],
                });
            }
        }
        
        Ok(violations)
    }
    
    /// Compute utilization efficiency
    fn compute_utilization_efficiency(&self, measurement: &PerformanceMeasurement<T>,
                                     platform: &HardwarePlatform) -> Result<T> {
        let compute_util = measurement.utilizations.get("compute").unwrap_or(&T::zero());
        let memory_util = measurement.utilizations.get("memory").unwrap_or(&T::zero());
        let bandwidth_util = measurement.utilizations.get("bandwidth").unwrap_or(&T::zero());
        
        // Weighted average of utilizations
        let efficiency = (*compute_util * scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()) + 
                         *memory_util * scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()) + 
                         *bandwidth_util * scirs2_core::numeric::NumCast::from(0.2).unwrap_or_else(|| T::zero()));
        
        Ok(efficiency)
    }
    
    /// Compute platform-specific score
    fn compute_platform_score(&self, measurement: &PerformanceMeasurement<T>,
                             platform: &HardwarePlatform) -> Result<T> {
        // Simple scoring based on multiple factors
        let latency_score = scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero()) / measurement.latency_ms.iter().cloned().fold(T::zero(), |acc, x| acc + x);
        let throughput_score = measurement.throughput / scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero());
        let efficiency_score = self.compute_utilization_efficiency(measurement, platform)?;
        
        let overall_score = (latency_score + throughput_score + efficiency_score) / scirs2_core::numeric::NumCast::from(3.0).unwrap_or_else(|| T::zero());
        Ok(overall_score.min(scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero())))
    }
    
    /// Identify system bottlenecks
    fn identify_bottlenecks(&self, measurement: &PerformanceMeasurement<T>,
                           platform: &HardwarePlatform) -> Result<Vec<Bottleneck>> {
        let mut bottlenecks = Vec::new();
        
        // Check compute utilization
        if let Some(&compute_util) = measurement.utilizations.get("compute") {
            if compute_util < scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()) {
                bottlenecks.push(Bottleneck {
                    bottleneck_type: BottleneckType::Compute,
                    severity: (scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()) - compute_util).to_f64().unwrap_or(0.0) * 3.33,
                    description: "Low compute utilization detected".to_string(),
                    affected_operations: vec!["conv2d".to_string(), "matmul".to_string()],
                });
            }
        }
        
        // Check memory utilization
        if let Some(&memory_util) = measurement.utilizations.get("memory") {
            if memory_util > scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()) {
                bottlenecks.push(Bottleneck {
                    bottleneck_type: BottleneckType::MemoryCapacity,
                    severity: (memory_util - scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero())).to_f64().unwrap_or(0.0) * 10.0,
                    description: "High memory utilization detected".to_string(),
                    affected_operations: vec!["large_tensors".to_string(), "activations".to_string()],
                });
            }
        }
        
        // Check thermal issues
        let max_temp = measurement.temperatures.iter().cloned().fold(T::zero(), T::max);
        if max_temp > scirs2_core::numeric::NumCast::from(80.0).unwrap_or_else(|| T::zero()) {
            bottlenecks.push(Bottleneck {
                bottleneck_type: BottleneckType::Thermal,
                severity: (max_temp - scirs2_core::numeric::NumCast::from(80.0).unwrap_or_else(|| T::zero())).to_f64().unwrap_or(0.0) / 20.0,
                description: "High temperature detected".to_string(),
                affected_operations: vec!["all_operations".to_string()],
            });
        }
        
        Ok(bottlenecks)
    }
    
    /// Validate prediction accuracy
    fn validate_prediction(&self, architecture_data: &HashMap<String, f64>,
                          measurement: &PerformanceMeasurement<T>,
                          platform: &HardwarePlatform) -> Result<T> {
        // Compare predicted vs actual performance
        let predicted_latency = architecture_data.get("estimated_latency").unwrap_or(&50.0);
        let actual_latency = measurement.latency_ms.iter().cloned().fold(T::zero(), |acc, x| acc + x) /
                           T::from(measurement.latency_ms.len() as f64).expect("unwrap failed");
        
        let error = (scirs2_core::numeric::NumCast::from(*predicted_latency).unwrap_or_else(|| T::zero()) - actual_latency).abs() / actual_latency;
        let accuracy = T::one() - error.min(T::one());
        
        Ok(accuracy.max(T::zero()))
    }
    
    /// Generate optimization recommendations
    fn generate_recommendations(&self, architecture_id: &str,
                               platform_results: &HashMap<String, PlatformEvaluationResult<T>>) -> Result<Vec<OptimizationRecommendation>> {
        let mut recommendations = Vec::new();
        
        // Analyze common bottlenecks across platforms
        for (platform_id, result) in platform_results {
            for bottleneck in &result.bottlenecks {
                match bottleneck.bottleneck_type {
                    BottleneckType::MemoryBandwidth => {
                        recommendations.push(OptimizationRecommendation {
                            recommendation_type: RecommendationType::MemoryOptimization,
                            description: "Optimize memory access patterns to improve bandwidth utilization".to_string(),
                            expected_improvement: {
                                let mut improvements = HashMap::new();
                                improvements.insert("latency".to_string(), 0.15);
                                improvements.insert("throughput".to_string(), 0.20);
                                improvements
                            },
                            difficulty: DifficultyLevel::Medium,
                            priority: 8,
                        });
                    }
                    BottleneckType::Compute => {
                        recommendations.push(OptimizationRecommendation {
                            recommendation_type: RecommendationType::OperationFusion,
                            description: "Fuse operations to improve compute utilization".to_string(),
                            expected_improvement: {
                                let mut improvements = HashMap::new();
                                improvements.insert("latency".to_string(), 0.25);
                                improvements.insert("utilization".to_string(), 0.30);
                                improvements
                            },
                            difficulty: DifficultyLevel::Hard,
                            priority: 9,
                        });
                    }
                    BottleneckType::MemoryCapacity => {
                        recommendations.push(OptimizationRecommendation {
                            recommendation_type: RecommendationType::Quantization,
                            description: "Apply quantization to reduce memory footprint".to_string(),
                            expected_improvement: {
                                let mut improvements = HashMap::new();
                                improvements.insert("memory".to_string(), 0.50);
                                improvements.insert("latency".to_string(), 0.10);
                                improvements
                            },
                            difficulty: DifficultyLevel::Easy,
                            priority: 7,
                        });
                    }
                    _ => {}
                }
            }
        }
        
        Ok(recommendations)
    }
    
    /// Get evaluation history
    pub fn get_evaluation_history(&self) -> &[HardwareEvaluation<T>] {
        &self.evaluation_history
    }
    
    /// Get cached measurements
    pub fn get_cached_measurements(&self) -> &HashMap<String, PerformanceMeasurement<T>> {
        &self.measurement_cache
    }
    
    /// Get profiling data
    pub fn get_profiling_data(&self) -> &HashMap<String, ProfilingData<T>> {
        &self.profiling_data
    }
}

impl Default for MeasurementConfig {
    fn default() -> Self {
        Self {
            warmup_runs: 5,
            measurement_runs: 20,
            precision_level: PrecisionLevel::Standard,
            batch_sizes: vec![1, 4, 8, 16, 32],
            input_shapes: vec![vec![224, 224, 3], vec![512, 512, 3]],
            timeout_seconds: 300,
        }
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> Default for HardwareConstraints<T> {
    fn default() -> Self {
        Self {
            max_latency: Some(scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero())), // 100ms
            max_memory: Some(2_000_000_000), // 2GB
            max_power: Some(scirs2_core::numeric::NumCast::from(150.0).unwrap_or_else(|| T::zero())), // 150W
            max_energy_per_inference: Some(scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero())), // 1J
            min_throughput: Some(scirs2_core::numeric::NumCast::from(10.0).unwrap_or_else(|| T::zero())), // 10 inferences/sec
            max_model_size: Some(100_000_000), // 100MB
            max_temperature: Some(scirs2_core::numeric::NumCast::from(85.0).unwrap_or_else(|| T::zero())), // 85°C
        }
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> Default for HardwareAwareConfig<T> {
    fn default() -> Self {
        Self {
            target_platforms: vec![
                HardwarePlatform {
                    id: "gpu_v100".to_string(),
                    name: "NVIDIA V100".to_string(),
                    hardware_type: HardwareType::GPU { cuda_cores: 5120, memory_gb: 32.0 },
                    compute_capabilities: ComputeCapabilities {
                        peak_flops: 15_000_000_000_000, // 15 TFLOPS
                        memory_bandwidth: 900.0, // GB/s
                        cache_sizes: vec![128*1024, 1024*1024], // L1, L2 cache
                        instruction_sets: vec!["CUDA".to_string(), "Tensor".to_string()],
                        execution_units: 80,
                        clock_frequency: 1530_000_000.0, // Hz
                    },
                    memory_specs: MemorySpecs {
                        total_memory: 34_000_000_000, // 34GB HBM2
                        available_memory: 32_000_000_000, // 32GB available
                        memory_types: vec!["HBM2".to_string()],
                        access_latencies: {
                            let mut latencies = HashMap::new();
                            latencies.insert("HBM2".to_string(), 350);
                            latencies
                        },
                        bandwidth_per_type: {
                            let mut bandwidths = HashMap::new();
                            bandwidths.insert("HBM2".to_string(), 900.0);
                            bandwidths
                        },
                    },
                    power_specs: PowerSpecs {
                        max_power: 300.0,
                        idle_power: 50.0,
                        efficiency: 50_000_000.0, // FLOPS/watt
                        tdp: 300.0,
                        power_states: vec![
                            PowerState {
                                name: "High Performance".to_string(),
                                power_consumption: 300.0,
                                performance_scale: 1.0,
                                transition_latency: 0.0,
                            },
                            PowerState {
                                name: "Balanced".to_string(),
                                power_consumption: 200.0,
                                performance_scale: 0.8,
                                transition_latency: 1000.0,
                            },
                        ],
                    },
                    optimizations: vec![
                        HardwareOptimization::Compiler("TensorRT".to_string()),
                        HardwareOptimization::MemoryAccess("Unified Memory".to_string()),
                    ],
                }
            ],
            constraints: HardwareConstraints::default(),
            performance_models: HashMap::new(),
            measurement_config: MeasurementConfig::default(),
            optimization_objectives: vec![
                HardwareObjective {
                    id: "latency".to_string(),
                    objective_type: HardwareObjectiveType::MinimizeLatency,
                    target_value: Some(scirs2_core::numeric::NumCast::from(50.0).unwrap_or_else(|| T::zero())),
                    weight: scirs2_core::numeric::NumCast::from(0.4).unwrap_or_else(|| T::zero()),
                    constraint_threshold: Some(scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero())),
                },
                HardwareObjective {
                    id: "throughput".to_string(),
                    objective_type: HardwareObjectiveType::MaximizeThroughput,
                    target_value: Some(scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero())),
                    weight: scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()),
                    constraint_threshold: Some(scirs2_core::numeric::NumCast::from(10.0).unwrap_or_else(|| T::zero())),
                },
                HardwareObjective {
                    id: "efficiency".to_string(),
                    objective_type: HardwareObjectiveType::MaximizeEfficiency,
                    target_value: None,
                    weight: scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()),
                    constraint_threshold: None,
                },
            ],
            energy_budget: None,
            thermal_constraints: None,
        }
    }
}