scirs2-neural 0.3.3

//! FPGA-specific hardware acceleration support

use crate::error::Result;
use scirs2_core::ndarray::prelude::*;
use std::collections::HashMap;
/// FPGA device configuration
#[derive(Debug, Clone)]
pub struct FPGAConfig {
    /// Device vendor (Xilinx, Intel/Altera, etc.)
    pub vendor: FPGAVendor,
    /// Device model
    pub model: String,
    /// Clock frequency in MHz
    pub clock_frequency: u32,
    /// Number of DSP slices
    pub dsp_slices: u32,
    /// Block RAM size in KB
    pub bram_size: u32,
    /// External memory bandwidth in GB/s
    pub memory_bandwidth: f32,
    /// Power budget in Watts
    pub power_budget: f32,
    /// Bitstream path
    pub bitstream_path: Option<String>,
}
/// FPGA vendor
#[derive(Debug, Clone, PartialEq)]
pub enum FPGAVendor {
    Xilinx,
    Intel,
    Lattice,
    Microsemi,
    Custom(String),
/// FPGA device implementation
pub struct FPGADevice {
    config: FPGAConfig,
    bitstream_loaded: bool,
    allocated_resources: ResourceAllocation,
    kernel_cache: HashMap<String, FPGAKernel>,
    /// Dynamic partial reconfiguration manager
    dpr_manager: Option<DPRManager>,
    /// Resource scheduler for temporal multiplexing
    resource_scheduler: ResourceScheduler,
    /// Performance profiler
    profiler: PerformanceProfiler,
impl FPGADevice {
    /// Create a new FPGA device
    pub fn new(config: FPGAConfig) -> Result<Self> {
        let dpr_manager = if config.vendor == FPGAVendor::Xilinx {
            Some(DPRManager::new()?)
        } else {
            None
        };
        Ok(Self {
            config,
            bitstream_loaded: false,
            allocated_resources: ResourceAllocation::default(),
            kernel_cache: HashMap::new(),
            dpr_manager,
            resource_scheduler: ResourceScheduler::new(),
            profiler: PerformanceProfiler::new(),
        })
    }
    /// Load bitstream to FPGA
    pub fn load_bitstream(&mut self, path: &str) -> Result<()> {
        // Simulate bitstream loading
        println!("Loading bitstream from: {}", path);
        self.bitstream_loaded = true;
        Ok(())
    /// Allocate resources for a kernel
    pub fn allocate_kernel(&mut self, kernel: &FPGAKernel) -> Result<ResourceAllocation> {
        let required = kernel.resource_requirements();
        // Check if resources are available
        if self.allocated_resources.dsp_slices + required.dsp_slices > self.config.dsp_slices {
            return Err(crate::error::NeuralError::ResourceExhausted(
                "Insufficient DSP slices".to_string(),
            ));
        }
        if self.allocated_resources.bram_blocks + required.bram_blocks > self.config.bram_size / 18
        {
                "Insufficient BRAM".to_string(),
        // Allocate resources
        self.allocated_resources.dsp_slices += required.dsp_slices;
        self.allocated_resources.bram_blocks += required.bram_blocks;
        self.allocated_resources.luts += required.luts;
        Ok(required)
    /// Execute a kernel on FPGA
    pub fn execute_kernel(
        &self,
        kernel: &FPGAKernel,
        input: &ArrayView2<f32>,
    ) -> Result<Array2<f32>> {
        if !self.bitstream_loaded {
            return Err(crate::error::NeuralError::InvalidState(
                "Bitstream not loaded".to_string(),
        // Simulate kernel execution
        let outputshape = kernel.compute_outputshape(input.shape());
        let mut output = Array2::zeros(outputshape);
        // Placeholder computation
        match &kernel.operation {
            FPGAOperation::MatMul { .. } => {
                // Simplified matrix multiplication
                output.fill(1.0);
            }
            FPGAOperation::Conv2D { .. } => {
                // Simplified convolution
                output.fill(0.5);
            FPGAOperation::Custom { .. } => {
                // Custom operation
                output.fill(0.0);
        Ok(output)
    /// Get resource utilization
    pub fn resource_utilization(&self) -> ResourceUtilization {
        ResourceUtilization {
            dsp_utilization: (self.allocated_resources.dsp_slices as f32
                / self.config.dsp_slices as f32)
                * 100.0,
            bram_utilization: (self.allocated_resources.bram_blocks as f32 * 18.0
                / self.config.bram_size as f32)
            lut_utilization: 0.0, // Would need total LUT count
            power_usage: self.estimate_power_usage(),
    /// Estimate power usage
    fn estimate_power_usage(&self) -> f32 {
        // Simple power model
        let base_power = 10.0; // Base power in Watts
        let dynamic_power = self.allocated_resources.dsp_slices as f32 * 0.1
            + self.allocated_resources.bram_blocks as f32 * 0.05;
        base_power + dynamic_power
/// FPGA kernel representation
#[derive(Clone)]
pub struct FPGAKernel {
    pub name: String,
    pub operation: FPGAOperation,
    pub pipeline_depth: u32,
    pub parallelism: u32,
    pub precision: PrecisionConfig,
impl FPGAKernel {
    /// Create a new FPGA kernel
    pub fn new(name: String, operation: FPGAOperation) -> Self {
        Self {
            name,
            operation,
            pipeline_depth: 1,
            parallelism: 1,
            precision: PrecisionConfig::default(),
    /// Get resource requirements
    pub fn resource_requirements(&self) -> ResourceAllocation {
        match &self.operation {
            FPGAOperation::MatMul { m, n, k } => {
                // Estimate resources for matrix multiplication
                let dsp_per_mac = 1;
                let parallel_macs = self.parallelism;
                ResourceAllocation {
                    dsp_slices: dsp_per_mac * parallel_macs,
                    bram_blocks: ((m * k + k * n) * 4 / 18432) as u32, // 18Kb blocks
                    luts: parallel_macs * 100,                         // Rough estimate
                    registers: parallel_macs * 200,
                }
            FPGAOperation::Conv2D {
                kernel_size,
                in_channels,
                out_channels,
                ..
            } => {
                // Estimate resources for convolution
                let kernel_elements = kernel_size * kernel_size * in_channels * out_channels;
                let dsp_slices = (kernel_elements / 4).min(512) as u32; // DSP packing
                    dsp_slices,
                    bram_blocks: (kernel_elements * 4 / 18432) as u32,
                    luts: dsp_slices * 150,
                    registers: dsp_slices * 300,
            FPGAOperation::Custom {
                resource_estimate, ..
            } => resource_estimate.clone(),
    /// Compute output shape
    fn compute_outputshape(&self, inputshape: &[usize]) -> (usize, usize) {
            FPGAOperation::MatMul { m, n, .. } => (*m, *n),
                stride,
                padding,
                let h = (inputshape[0] + 2 * padding - kernel_size) / stride + 1;
                let w = (inputshape[1] + 2 * padding - kernel_size) / stride + 1;
                (h * w, *out_channels)
            FPGAOperation::Custom { outputshape, .. } => *outputshape,
    /// Optimize kernel for specific FPGA
    pub fn optimize_for_device(&mut self, device: &FPGADevice) -> Result<()> {
        // Adjust parallelism based on available resources
        let available_dsp = device.config.dsp_slices - device.allocated_resources.dsp_slices;
        let max_parallelism = available_dsp / 4; // Rough estimate
        self.parallelism = self.parallelism.min(max_parallelism);
        // Adjust pipeline depth for latency/throughput trade-off
        self.pipeline_depth = match &self.operation {
            FPGAOperation::MatMul { .. } => 8,
            FPGAOperation::Conv2D { .. } => 16,
            FPGAOperation::Custom { .. } => 4,
/// FPGA operation types
pub enum FPGAOperation {
    MatMul {
        m: usize,
        n: usize,
        k: usize,
    },
    Conv2D {
        kernel_size: usize,
        stride: usize,
        padding: usize,
        in_channels: usize,
        out_channels: usize,
    Custom {
        description: String,
        compute_function: String,
        resource_estimate: ResourceAllocation,
        outputshape: (usize, usize),
/// Resource allocation
#[derive(Debug, Clone, Default)]
pub struct ResourceAllocation {
    pub bram_blocks: u32,
    pub luts: u32,
    pub registers: u32,
/// Resource utilization metrics
pub struct ResourceUtilization {
    pub dsp_utilization: f32,
    pub bram_utilization: f32,
    pub lut_utilization: f32,
    pub power_usage: f32,
/// Precision configuration for FPGA kernels
pub struct PrecisionConfig {
    pub input_bits: u8,
    pub weight_bits: u8,
    pub accumulator_bits: u8,
    pub output_bits: u8,
impl Default for PrecisionConfig {
    fn default() -> Self {
            input_bits: 16,
            weight_bits: 16,
            accumulator_bits: 32,
            output_bits: 16,
/// FPGA kernel compiler
pub struct FPGACompiler {
    target_device: FPGAConfig,
    optimization_level: OptimizationLevel,
impl FPGACompiler {
    /// Create a new FPGA compiler
    pub fn new(_target_device: FPGAConfig, optimizationlevel: OptimizationLevel) -> Self {
            target_device,
            optimization_level,
    /// Compile a high-level operation to FPGA kernel
    pub fn compile_operation(
        operation: &str,
        params: &HashMap<String, f32>,
    ) -> Result<FPGAKernel> {
        match operation {
            "matmul" => {
                let m = params.get("m").copied().unwrap_or(32.0) as usize;
                let n = params.get("n").copied().unwrap_or(32.0) as usize;
                let k = params.get("k").copied().unwrap_or(32.0) as usize;
                Ok(FPGAKernel::new(
                    "matmul_kernel".to_string(),
                    FPGAOperation::MatMul { m, n, k },
                ))
            "conv2d" => {
                let kernel_size = params.get("kernel_size").copied().unwrap_or(3.0) as usize;
                let stride = params.get("stride").copied().unwrap_or(1.0) as usize;
                let padding = params.get("padding").copied().unwrap_or(1.0) as usize;
                let in_channels = params.get("in_channels").copied().unwrap_or(3.0) as usize;
                let out_channels = params.get("out_channels").copied().unwrap_or(64.0) as usize;
                    "conv2d_kernel".to_string(),
                    FPGAOperation::Conv2D {
                        kernel_size,
                        stride,
                        padding,
                        in_channels,
                        out_channels,
                    }_ => Err(crate::error::NeuralError::NotImplemented(format!(
                "Operation {} not supported for FPGA",
                operation
            ))),
    /// Generate HLS code for kernel
    pub fn generate_hls(&self, kernel: &FPGAKernel) -> Result<String> {
        let mut code = String::new();
        // Add HLS pragmas
        code.push_str("#include <hls_stream.h>\n");
        code.push_str("#include <ap_fixed.h>\n\n");
                code.push_str(&format!(
                    "void matmul_kernel(float A[{}][{}], float B[{}][{}], float C[{}][{}]) {{\n",
                    m, k, k, n, m, n
                ));
                code.push_str("    #pragma HLS INTERFACE m_axi port=A,B,C\n");
                code.push_str("    #pragma HLS PIPELINE II=1\n");
                code.push_str("    // Matrix multiplication implementation\n");
                code.push_str("}\n");
                code.push_str("void conv2d_kernel(...) {\n");
                code.push_str("    #pragma HLS INTERFACE m_axi port=input,weights,output\n");
                code.push_str("    #pragma HLS PIPELINE\n");
                code.push_str("    // Convolution implementation\n");
                compute_function, ..
                code.push_str(compute_function);
        Ok(code)
/// Optimization level for FPGA compilation
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum OptimizationLevel {
    O0, // No optimization
    O1, // Basic optimization
    O2, // Standard optimization
    O3, // Aggressive optimization
/// Dynamic Partial Reconfiguration Manager
pub struct DPRManager {
    /// Available partial regions
    partial_regions: Vec<PartialRegion>,
    /// Currently loaded modules
    loaded_modules: HashMap<String, PartialBitstream>,
    /// Reconfiguration queue
    reconfig_queue: Vec<ReconfigRequest>,
impl DPRManager {
    pub fn new() -> Result<Self> {
            partial_regions: vec![
                PartialRegion {
                    id: 0,
                    location: (0, 0, 100, 100),
                    status: RegionStatus::Available,
                },
                    id: 1,
                    location: (0, 100, 100, 200),
            ],
            loaded_modules: HashMap::new(),
            reconfig_queue: Vec::new(),
    /// Schedule a partial reconfiguration
    pub fn schedule_reconfiguration(
        &mut self,
        module_name: String,
        bitstream: PartialBitstream,
        priority: ReconfigPriority,
    ) -> Result<u32> {
        let request_id = self.reconfig_queue.len() as u32;
        self.reconfig_queue.push(ReconfigRequest {
            id: request_id,
            module_name,
            bitstream,
            priority,
            timestamp: std::time::Instant::now(),
        });
        // Sort by priority
        self.reconfig_queue
            .sort_by(|a, b| b.priority.cmp(&a.priority));
        Ok(request_id)
    /// Execute pending reconfigurations
    pub fn execute_reconfigurations(&mut self) -> Result<Vec<ReconfigResult>> {
        let mut results = Vec::new();
        while let Some(request) = self.reconfig_queue.pop() {
            let start_time = std::time::Instant::now();
            // Find available region
            if let Some(region) = self
                .partial_regions
                .iter_mut()
                .find(|r| r.status == RegionStatus::Available)
            {
                region.status = RegionStatus::Reconfiguring;
                // Simulate reconfiguration time
                std::thread::sleep(std::time::Duration::from_millis(100));
                // Load the module
                self.loaded_modules
                    .insert(request.module_name.clone(), request.bitstream.clone());
                region.status = RegionStatus::Loaded(request.module_name.clone());
                results.push(ReconfigResult {
                    request_id: request.id,
                    success: true,
                    duration: start_time.elapsed(),
                    region_id: Some(region.id),
                    error_message: None,
                });
            } else {
                    success: false,
                    region_id: None,
                    error_message: Some("No available regions".to_string()),
        Ok(results)
/// Partial region in FPGA
pub struct PartialRegion {
    pub id: u32,
    pub location: (u32, u32, u32, u32), // (x, y, width, height)
    pub status: RegionStatus,
/// Status of a partial region
pub enum RegionStatus {
    Available,
    Reconfiguring,
    Loaded(String),
    Error(String),
/// Partial bitstream
pub struct PartialBitstream {
    pub data: Vec<u8>,
    pub size: usize,
    pub module_name: String,
    pub resource_requirements: ResourceAllocation,
/// Reconfiguration request
pub struct ReconfigRequest {
    pub bitstream: PartialBitstream,
    pub priority: ReconfigPriority,
    pub timestamp: std::time::Instant,
/// Reconfiguration priority
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum ReconfigPriority {
    Low = 1,
    Medium = 2,
    High = 3,
    Critical = 4,
/// Reconfiguration result
pub struct ReconfigResult {
    pub request_id: u32,
    pub success: bool,
    pub duration: std::time::Duration,
    pub region_id: Option<u32>,
    pub error_message: Option<String>,
/// Resource scheduler for temporal multiplexing
pub struct ResourceScheduler {
    /// Scheduled tasks
    task_queue: Vec<ScheduledTask>,
    /// Currently executing tasks
    executing_tasks: Vec<ExecutingTask>,
    /// Scheduler strategy
    strategy: SchedulingStrategy,
impl ResourceScheduler {
    pub fn new() -> Self {
            task_queue: Vec::new(),
            executing_tasks: Vec::new(),
            strategy: SchedulingStrategy::RoundRobin,
    /// Schedule a kernel execution
    pub fn schedule_kernel(
        kernel: FPGAKernel,
        input_data: Array2<f32>,
        priority: TaskPriority,
        let task_id = self.task_queue.len() as u32;
        let task = ScheduledTask {
            id: task_id,
            kernel,
            input_data,
            submission_time: std::time::Instant::now(),
            estimated_duration: std::time::Duration::from_millis(100),
        self.task_queue.push(task);
        self.sort_task_queue();
        Ok(task_id)
    /// Sort task queue based on scheduling strategy
    fn sort_task_queue(&mut self) {
        match self.strategy {
            SchedulingStrategy::Priority => {
                self.task_queue.sort_by(|a, b| b.priority.cmp(&a.priority));
            SchedulingStrategy::ShortestJobFirst => {
                self.task_queue
                    .sort_by(|a, b| a.estimated_duration.cmp(&b.estimated_duration));
            SchedulingStrategy::EarliestDeadlineFirst => {
                // Would implement deadline-based sorting
                    .sort_by(|a, b| a.submission_time.cmp(&b.submission_time));
            SchedulingStrategy::RoundRobin => {
                // FIFO for round-robin
    /// Execute next scheduled task
    pub fn execute_next_task(&mut self) -> Result<Option<TaskResult>> {
        if let Some(task) = self.task_queue.pop() {
            // Simulate task execution
            let executing_task = ExecutingTask {
                id: task.id,
                kernel: task.kernel.clone(),
                start_time,
                estimated_completion: start_time + task.estimated_duration,
            };
            self.executing_tasks.push(executing_task);
            // For simulation, complete immediately
            let duration = std::time::Duration::from_millis(50);
            let outputshape = task.kernel.compute_outputshape(&[32, 32]);
            let output = Array2::zeros(outputshape);
            Ok(Some(TaskResult {
                task_id: task.id,
                output,
                execution_time: duration,
                success: true,
                error_message: None,
            }))
            Ok(None)
/// Scheduled task
pub struct ScheduledTask {
    pub kernel: FPGAKernel,
    pub input_data: Array2<f32>,
    pub priority: TaskPriority,
    pub submission_time: std::time::Instant,
    pub estimated_duration: std::time::Duration,
/// Currently executing task
pub struct ExecutingTask {
    pub start_time: std::time::Instant,
    pub estimated_completion: std::time::Instant,
/// Task execution result
pub struct TaskResult {
    pub task_id: u32,
    pub output: Array2<f32>,
    pub execution_time: std::time::Duration,
/// Task priority
pub enum TaskPriority {
/// Scheduling strategy
pub enum SchedulingStrategy {
    Priority,
    RoundRobin,
    ShortestJobFirst,
    EarliestDeadlineFirst,
/// Performance profiler for FPGA operations
pub struct PerformanceProfiler {
    /// Execution history
    execution_history: Vec<ProfileEntry>,
    /// Performance metrics
    metrics: PerformanceMetrics,
impl PerformanceProfiler {
            execution_history: Vec::new(),
            metrics: PerformanceMetrics::default(),
    /// Record kernel execution
    pub fn record_execution(
        kernel_name: String,
        execution_time: std::time::Duration,
        throughput: f32,
        resource_usage: ResourceUtilization,
    ) {
        let entry = ProfileEntry {
            kernel_name: kernel_name.clone(),
            execution_time,
            throughput,
            resource_usage,
        self.execution_history.push(entry);
        self.update_metrics(&kernel_name, execution_time, throughput);
    /// Update performance metrics
    fn update_metrics(&mut self, kernelname: &str, time: std::time::Duration, throughput: f32) {
        self.metrics.total_executions += 1;
        self.metrics.total_time += time;
        if throughput > self.metrics.peak_throughput {
            self.metrics.peak_throughput = throughput;
            self.metrics.best_kernel = Some(kernel_name.to_string());
        // Update per-kernel statistics
        let stats = self
            .metrics
            .per_kernel_stats
            .entry(kernel_name.to_string())
            .or_insert_with(KernelStats::default);
        stats.execution_count += 1;
        stats.total_time += time;
        stats.avg_time = stats.total_time / stats.execution_count as u32;
        stats.max_throughput = stats.max_throughput.max(throughput);
    /// Get performance report
    pub fn generate_report(&self) -> PerformanceReport {
        PerformanceReport {
            total_kernels: self.metrics.per_kernel_stats.len(),
            total_executions: self.metrics.total_executions,
            avg_execution_time: if self.metrics.total_executions > 0 {
                self.metrics.total_time / self.metrics.total_executions as u32
                std::time::Duration::ZERO
            },
            peak_throughput: self.metrics.peak_throughput,
            best_performing_kernel: self.metrics.best_kernel.clone(),
            bottlenecks: self.identify_bottlenecks(),
    /// Identify performance bottlenecks
    fn identify_bottlenecks(&self) -> Vec<String> {
        let mut bottlenecks = Vec::new();
        for (kernel_name, stats) in &self.metrics.per_kernel_stats {
            if stats.avg_time > std::time::Duration::from_millis(100) {
                bottlenecks.push(format!(
                    "Kernel {} has high average execution time",
                    kernel_name
            if stats.max_throughput < 1000.0 {
                bottlenecks.push(format!("Kernel {} has low throughput", kernel_name));
        bottlenecks
/// Profile entry
pub struct ProfileEntry {
    pub kernel_name: String,
    pub throughput: f32,
    pub resource_usage: ResourceUtilization,
/// Performance metrics
pub struct PerformanceMetrics {
    pub total_executions: usize,
    pub total_time: std::time::Duration,
    pub peak_throughput: f32,
    pub best_kernel: Option<String>,
    pub per_kernel_stats: HashMap<String, KernelStats>,
/// Per-kernel statistics
pub struct KernelStats {
    pub execution_count: usize,
    pub avg_time: std::time::Duration,
    pub max_throughput: f32,
/// Performance report
pub struct PerformanceReport {
    pub total_kernels: usize,
    pub avg_execution_time: std::time::Duration,
    pub best_performing_kernel: Option<String>,
    pub bottlenecks: Vec<String>,
#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_fpga_device_creation() {
        let config = FPGAConfig {
            vendor: FPGAVendor::Xilinx,
            model: "xcu250".to_string(),
            clock_frequency: 300,
            dsp_slices: 12288,
            bram_size: 54000,
            memory_bandwidth: 77.0,
            power_budget: 225.0,
            bitstream_path: None,
        let device = FPGADevice::new(config).expect("Operation failed");
        assert!(!device.bitstream_loaded);
    fn test_fpga_kernel_resources() {
        let kernel = FPGAKernel::new(
            "test_matmul".to_string(),
            FPGAOperation::MatMul {
                m: 128,
                n: 128,
                k: 128,
        );
        let resources = kernel.resource_requirements();
        assert!(resources.dsp_slices > 0);
        assert!(resources.bram_blocks > 0);
    fn test_fpga_compiler() {
            vendor: FPGAVendor::Intel,
            model: "stratix10".to_string(),
            clock_frequency: 400,
            dsp_slices: 5760,
            bram_size: 240000,
            memory_bandwidth: 128.0,
            power_budget: 150.0,
        let compiler = FPGACompiler::new(config, OptimizationLevel::O2);
        let mut params = HashMap::new();
        params.insert("m".to_string(), 64.0);
        params.insert("n".to_string(), 64.0);
        params.insert("k".to_string(), 64.0);
        let kernel = compiler.compile_operation("matmul", &params).expect("Operation failed");
        assert_eq!(kernel.name, "matmul_kernel");
        let hls_code = compiler.generate_hls(&kernel).expect("Operation failed");
        assert!(hls_code.contains("matmul_kernel"));
        assert!(hls_code.contains("#pragma HLS"));