scirs2-neural 0.4.2

//! Custom ASIC Support
//!
//! This module provides support for custom Application-Specific Integrated Circuits (ASICs)
//! designed for neural network acceleration, including neural processing units (NPUs),
//! tensor processing units (TPUs), and other domain-specific accelerators.

use crate::error::Result;
use crate::hardware::accelerator::DeviceBuffer;
use crate::hardware::accelerator::{ComputeStream, Kernel, MemoryInfo, ProfilingInfo};
use crate::hardware::{Accelerator, AcceleratorCapabilities, AcceleratorType};
use scirs2_core::ndarray::prelude::*;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
/// Custom ASIC configuration
#[derive(Debug, Clone)]
pub struct ASICConfig {
    /// ASIC vendor
    pub vendor: String,
    /// ASIC model
    pub model: String,
    /// Architecture version
    pub architecture_version: String,
    /// Number of processing elements
    pub processing_elements: u32,
    /// Memory hierarchy configuration
    pub memory_hierarchy: MemoryHierarchy,
    /// Supported data types
    pub supported_datatypes: Vec<DataType>,
    /// Native operations
    pub native_operations: Vec<NativeOperation>,
    /// Power characteristics
    pub power_profile: PowerProfile,
    /// Interconnect topology
    pub interconnect: InterconnectTopology,
}
/// Memory hierarchy for custom ASIC
pub struct MemoryHierarchy {
    /// On-chip SRAM levels
    pub sram_levels: Vec<MemoryLevel>,
    /// External memory configuration
    pub external_memory: ExternalMemoryConfig,
    /// Cache configuration
    pub cache_config: CacheConfig,
/// Memory level in the hierarchy
pub struct MemoryLevel {
    /// Level name (L1, L2, etc.)
    pub name: String,
    /// Size in bytes
    pub size: usize,
    /// Access latency in cycles
    pub latency_cycles: u32,
    /// Bandwidth in GB/s
    pub bandwidth: f32,
    /// Whether it's shared between PEs
    pub shared: bool,
/// External memory configuration
pub struct ExternalMemoryConfig {
    /// Memory type (HBM, GDDR, DDR)
    pub memory_type: String,
    /// Total capacity in bytes
    pub capacity: usize,
    /// Access latency in nanoseconds
    pub latency_ns: f32,
/// Cache configuration
pub struct CacheConfig {
    /// Instruction cache size
    pub icache_size: usize,
    /// Data cache size
    pub dcache_size: usize,
    /// Cache line size
    pub line_size: usize,
    /// Associativity
    pub associativity: u32,
/// Supported data types
#[derive(Debug, Clone, PartialEq)]
pub enum DataType {
    /// 32-bit floating point
    Float32,
    /// 16-bit floating point
    Float16,
    /// BFloat16
    BFloat16,
    /// 8-bit integer
    Int8,
    /// 4-bit integer
    Int4,
    /// Binary (1-bit)
    Binary,
    /// Custom fixed-point
    FixedPoint {
        integer_bits: u8,
        fractional_bits: u8,
    },
    /// Posit arithmetic
    Posit { nbits: u8, es: u8 },
/// Native operations supported by the ASIC
pub enum NativeOperation {
    /// Matrix multiplication
    MatMul {
        tile_sizes: Vec<(usize, usize)>,
        datatypes: Vec<DataType>,
    /// Convolution
    Convolution {
        kernel_sizes: Vec<usize>,
        strides: Vec<usize>,
    /// Activation functions
    Activation {
        functions: Vec<ActivationFunction>,
    /// Reduction operations
    Reduction {
        operations: Vec<ReductionType>,
    /// Elementwise operations
    ElementWise {
        operations: Vec<ElementWiseOperation>,
    /// Custom operation
    Custom {
        name: String,
        instruction_encoding: Vec<u8>,
        latency_cycles: u32,
/// Activation functions
pub enum ActivationFunction {
    ReLU,
    Sigmoid,
    Tanh,
    GELU,
    Swish,
    Custom(String),
/// Reduction types
pub enum ReductionType {
    Sum,
    Mean,
    Max,
    Min,
    ArgMax,
    ArgMin,
/// Element-wise operations
pub enum ElementWiseOperation {
    Add,
    Subtract,
    Multiply,
    Divide,
    Comparison,
    Bitwise,
/// Power profile for the ASIC
pub struct PowerProfile {
    /// Idle power in Watts
    pub idle_power: f32,
    /// Peak power in Watts
    pub peak_power: f32,
    /// Dynamic power per operation in nJ
    pub dynamic_power_per_op: f32,
    /// Power efficiency in TOPS/W
    pub efficiency_tops_per_watt: f32,
/// Interconnect topology
pub struct InterconnectTopology {
    /// Topology type
    pub topology_type: TopologyType,
    /// Network-on-Chip configuration
    pub noc_config: Option<NoCConfig>,
    /// Bus configuration
    pub bus_config: Option<BusConfig>,
/// Topology types
pub enum TopologyType {
    Mesh2D { width: u32, height: u32 },
    Torus2D { width: u32, height: u32 },
    Ring,
    Crossbar,
    Tree,
/// Network-on-Chip configuration
pub struct NoCConfig {
    /// Router latency in cycles
    pub router_latency: u32,
    /// Link bandwidth in bits per cycle
    pub link_bandwidth: u32,
    /// Buffer depth
    pub buffer_depth: u32,
    /// Flow control type
    pub flow_control: FlowControlType,
/// Flow control types
pub enum FlowControlType {
    CreditBased,
    Wormhole,
    StoreAndForward,
/// Bus configuration
pub struct BusConfig {
    /// Bus width in bits
    pub width: u32,
    /// Clock frequency in MHz
    pub frequency: u32,
    /// Arbitration scheme
    pub arbitration: ArbitrationType,
/// Arbitration types
pub enum ArbitrationType {
    RoundRobin,
    Priority,
    TDMA,
    Weighted,
/// Custom ASIC device implementation
pub struct CustomASIC {
    config: ASICConfig,
    capabilities: AcceleratorCapabilities,
    memory_manager: Arc<Mutex<ASICMemoryManager>>,
    instruction_cache: Arc<Mutex<InstructionCache>>,
    performance_counters: Arc<Mutex<PerformanceCounters>>,
    runtime_state: Arc<Mutex<RuntimeState>>,
impl CustomASIC {
    /// Create a new custom ASIC device
    pub fn new(config: ASICConfig) -> Result<Self> {
        let capabilities = Self::build_capabilities(&_config);
        let memory_manager = ASICMemoryManager::new(&_config.memory_hierarchy);
        let instruction_cache =
            InstructionCache::new(_config.memory_hierarchy.cache_config.icache_size);
        let performance_counters = PerformanceCounters::new();
        let runtime_state = RuntimeState::new();
        Ok(Self {
            config,
            capabilities,
            memory_manager: Arc::new(Mutex::new(memory_manager)),
            instruction_cache: Arc::new(Mutex::new(instruction_cache)),
            performance_counters: Arc::new(Mutex::new(performance_counters)),
            runtime_state: Arc::new(Mutex::new(runtime_state)),
        })
    }
    /// Build accelerator capabilities from ASIC config
    fn build_capabilities(config: &ASICConfig) -> AcceleratorCapabilities {
        // Calculate peak performance estimates
        let peak_ops_per_cycle = config.processing_elements as f32 * 2.0; // Estimate
        let clock_freq_ghz = 1.0; // Assume 1 GHz for simplicity
        let peak_tflops_fp32 = peak_ops_per_cycle * clock_freq_ghz;
        // Estimate memory bandwidth
        let total_bandwidth = config.memory_hierarchy.external_memory.bandwidth
            + _config
                .memory_hierarchy
                .sram_levels
                .iter()
                .map(|level| level.bandwidth)
                .sum::<f32>();
        AcceleratorCapabilities {
            name: format!("{} {}", config.vendor, config.model),
            compute_capability: (1, 0), // Custom versioning
            total_memory: config.memory_hierarchy.external_memory.capacity,
            memory_bandwidth: total_bandwidth,
            compute_units: config.processing_elements,
            peak_tflops_fp32,
            peak_tflops_fp16: peak_tflops_fp32 * 2.0,
            peak_tflops_int8: peak_tflops_fp32 * 4.0,
            features: crate::hardware::accelerator::AcceleratorFeatures {
                mixed_precision: config.supported_datatypes.len() > 1,
                tensor_cores: config
                    .native_operations
                    .iter()
                    .any(|op| matches!(op, NativeOperation::MatMul { .. })),
                sparse_ops: false, // Would need to check config
                unified_memory: true,
                multi_device: false,
                graph_optimization: true,
                dynamicshapes: true,
                custom_kernels: true,
            },
        }
    /// Compile a high-level operation to ASIC instruction sequence
    pub fn compile_operation(&self, operation: &ASICOperation) -> Result<ASICProgram> {
        let mut program = ASICProgram::new();
        match operation {
            ASICOperation::MatMul { m, n, k, datatype } => {
                // Find optimal tiling strategy
                let tile_config = self.find_optimal_tiling(*m, *n, *k)?;
                // Generate instruction sequence
                for tile in &tile_config.tiles {
                    // Load data instructions
                    program.add_instruction(ASICInstruction::LoadMatrix {
                        src_addr: tile.a_addr,
                        dst_pe: tile.pe_id,
                        rows: tile.rows,
                        cols: tile.cols,
                    });
                        src_addr: tile.b_addr,
                    // Compute instruction
                    program.add_instruction(ASICInstruction::MatMul {
                        pe_id: tile.pe_id,
                        accumulate: tile.accumulate,
                    // Store result
                    program.add_instruction(ASICInstruction::StoreMatrix {
                        src_pe: tile.pe_id,
                        dst_addr: tile.c_addr,
                }
                // Add synchronization
                program.add_instruction(ASICInstruction::Synchronize);
            }
            ASICOperation::Convolution {
                inputshape,
                kernelshape,
                stride,
                padding,
                datatype,
            } => {
                let (batch_size, input_channels, input_height, input_width) = *inputshape;
                let (output_channels, kernel_channels, kernel_height, kernel_width) = *kernelshape;
                let (stride_h, stride_w) = *stride;
                let (padding_h, padding_w) = *padding;
                // Validate dimensions
                if input_channels != kernel_channels {
                    return Err(crate::error::NeuralError::InvalidArgument(format!(
                        "Input channels ({}) must match kernel channels ({})",
                        input_channels, kernel_channels
                    )));
                // Calculate output dimensions
                let output_height = (input_height + 2 * padding_h - kernel_height) / stride_h + 1;
                let output_width = (input_width + 2 * padding_w - kernel_width) / stride_w + 1;
                // Load convolution kernel to processing elements
                // Distribute kernels across available PEs
                let kernels_per_pe =
                    (output_channels + self.self.config.processing_elements as usize - 1)
                        / self.self.config.processing_elements as usize;
                for pe_id in 0..self.config.processing_elements as usize {
                    let start_channel = pe_id * kernels_per_pe;
                    let end_channel =
                        std::cmp::min(start_channel + kernels_per_pe, output_channels);
                    if start_channel < output_channels {
                        program.add_instruction(ASICInstruction::LoadConvKernel {
                            src_addr: start_channel
                                * kernel_channels
                                * kernel_height
                                * kernel_width,
                            dst_pe: pe_id,
                            kernel_height,
                            kernel_width,
                            input_channels: kernel_channels,
                            output_channels: end_channel - start_channel,
                        });
                    }
                // Process each batch item
                for batch_idx in 0..batch_size {
                    // Load input data for this batch
                    let input_offset = batch_idx * input_channels * input_height * input_width;
                    program.add_instruction(ASICInstruction::LoadConvInput {
                        src_addr: input_offset,
                        dst_pe: 0, // Input is shared across PEs
                        height: input_height,
                        width: input_width,
                        channels: input_channels,
                    // Execute convolution on each PE
                    for pe_id in 0..self.config.processing_elements as usize {
                        program.add_instruction(ASICInstruction::Convolution {
                            pe_id,
                            stride_h,
                            stride_w,
                            padding_h,
                            padding_w,
                            accumulate: false,
                    // Store results
                    let output_offset = batch_idx * output_channels * output_height * output_width;
                        let start_channel = pe_id * kernels_per_pe;
                        if start_channel < output_channels {
                            let channels_this_pe =
                                std::cmp::min(kernels_per_pe, output_channels - start_channel);
                            program.add_instruction(ASICInstruction::StoreMatrix {
                                src_pe: pe_id,
                                dst_addr: output_offset
                                    + start_channel * output_height * output_width,
                                rows: output_height,
                                cols: output_width * channels_this_pe,
                            });
                        }
            ASICOperation::Custom { name, parameters } => {
                // Custom operation compilation framework
                match name.as_str() {
                    "elementwise_add" => {
                        // Example: Element-wise addition
                        let opcode = 0x1000; // Custom opcode for elementwise add
                        let size = parameters.get("size").unwrap_or(&0.0) as &f32;
                        program.add_instruction(ASICInstruction::Custom {
                            opcode,
                            operands: vec![*size as u32],
                    "elementwise_mul" => {
                        // Example: Element-wise multiplication
                        let opcode = 0x1001; // Custom opcode for elementwise mul
                    "activation_relu" => {
                        // Example: ReLU activation
                        let opcode = 0x2000; // Custom opcode for ReLU
                    "activation_sigmoid" => {
                        // Example: Sigmoid activation
                        let opcode = 0x2001; // Custom opcode for Sigmoid
                    "pooling_max" => {
                        // Example: Max pooling
                        let opcode = 0x3000; // Custom opcode for max pooling
                        let kernel_size = parameters.get("kernel_size").unwrap_or(&2.0) as &f32;
                        let stride = parameters.get("stride").unwrap_or(&2.0) as &f32;
                        let input_height = parameters.get("input_height").unwrap_or(&0.0) as &f32;
                        let input_width = parameters.get("input_width").unwrap_or(&0.0) as &f32;
                            operands: vec![
                                *kernel_size as u32,
                                *stride as u32,
                                *input_height as u32,
                                *input_width as u32,
                            ],
                    "batch_norm" => {
                        // Example: Batch normalization
                        let opcode = 0x4000; // Custom opcode for batch norm
                        let channels = parameters.get("channels").unwrap_or(&0.0) as &f32;
                        let epsilon = parameters.get("epsilon").unwrap_or(&1e-5) as &f32;
                                *channels as u32,
                                (*epsilon * 1e6) as u32, // Scale epsilon for integer representation
                    _ => {
                        // For unrecognized custom operations, provide a framework for extension
                        // Use a generic custom opcode and encode the name hash
                        use std::collections::hash_map::DefaultHasher;
                        use std::hash::{Hash, Hasher};
                        let mut hasher = DefaultHasher::new();
                        name.hash(&mut hasher);
                        let name_hash = hasher.finish() as u32;
                        let opcode = 0x9000 | (name_hash & 0x0FFF); // Generic custom opcode with name hash
                        // Encode parameters as operands (simplified)
                        let mut operands = vec![name_hash];
                        for (key, value) in parameters {
                            let mut key_hasher = DefaultHasher::new();
                            key.hash(&mut key_hasher);
                            let key_hash = key_hasher.finish() as u32;
                            operands.push(key_hash);
                            operands.push((*value * 1000.0) as u32); // Scale float to integer
                        program.add_instruction(ASICInstruction::Custom { opcode, operands });
                        // Log that this is an unrecognized operation for debugging
                        eprintln!("Warning: Unrecognized custom operation '{}' compiled with generic handler", name);
                // Add synchronization after custom operations
        Ok(program)
    /// Execute an ASIC program
    pub fn execute_program(&self, program: &ASICProgram) -> Result<()> {
        let mut counters = self.performance_counters.lock().map_err(|e| {
            crate::error::NeuralError::DeviceError(format!(
                "Failed to lock performance counters: {}",
                e
            ))
        })?;
        let start_time = std::time::Instant::now();
        for instruction in &program.instructions {
            self.execute_instruction(instruction)?;
            counters.instructions_executed += 1;
        counters.execution_time += start_time.elapsed();
        Ok(())
    /// Execute a single instruction
    fn execute_instruction(&self, instruction: &ASICInstruction) -> Result<()> {
        match instruction {
            ASICInstruction::LoadMatrix { .. } => {
                // Simulate memory load
                std::thread::sleep(std::time::Duration::from_nanos(100));
            ASICInstruction::StoreMatrix { .. } => {
                // Simulate memory store
            ASICInstruction::MatMul { .. } => {
                // Simulate matrix multiplication
                std::thread::sleep(std::time::Duration::from_nanos(200));
            ASICInstruction::Synchronize => {
                // Synchronize all processing elements
                std::thread::sleep(std::time::Duration::from_nanos(50));
            ASICInstruction::Custom { .. } => {
                // Custom instruction execution
    /// Find optimal tiling strategy for matrix multiplication
    fn find_optimal_tiling(&self, m: usize, n: usize, k: usize) -> Result<TilingConfig> {
        // Simple tiling strategy based on available PEs and memory
        let num_pes = self.self.config.processing_elements as usize;
        let tile_size = 64; // Default tile size
        let mut tiles = Vec::new();
        let mut pe_id = 0;
        for i in (0..m).step_by(tile_size) {
            for j in (0..n).step_by(tile_size) {
                let tile_rows = tile_size.min(m - i);
                let tile_cols = tile_size.min(n - j);
                tiles.push(MatMulTile {
                    pe_id: pe_id % num_pes,
                    rows: tile_rows,
                    cols: tile_cols,
                    a_addr: i * k * 4, // Assuming float32
                    b_addr: j * k * 4,
                    c_addr: i * n * 4 + j * 4,
                    accumulate: false,
                });
                pe_id += 1;
        Ok(TilingConfig { tiles })
    /// Get performance statistics
    pub fn get_performance_stats(&self) -> Result<PerformanceStats> {
        let counters = self.performance_counters.lock().map_err(|e| {
        Ok(PerformanceStats {
            instructions_executed: counters.instructions_executed,
            total_execution_time: counters.execution_time,
            average_ipc: if counters.execution_time.as_nanos() > 0 {
                (counters.instructions_executed as f64) / counters.execution_time.as_secs_f64()
            } else {
                0.0
            memory_accesses: counters.memory_accesses,
            cache_hits: counters.cache_hits,
            cache_misses: counters.cache_misses,
            power_consumption: self.estimate_power_consumption(),
    /// Estimate current power consumption
    fn estimate_power_consumption(&self) -> f32 {
        let counters = match self.performance_counters.lock() {
            Ok(counters) => counters,
            Err(_) => {
                // Return base power if lock fails
                return self.config.power_profile.idle_power;
        };
        let base_power = self.config.power_profile.idle_power;
        // Simple power model based on instruction execution rate
        let dynamic_power = if counters.execution_time.as_secs_f64() > 0.0 {
            let ipc =
                (counters.instructions_executed as f64) / counters.execution_time.as_secs_f64();
            ipc as f32 * self.config.power_profile.dynamic_power_per_op
        } else {
            0.0
        base_power + dynamic_power
/// High-level ASIC operations
pub enum ASICOperation {
        m: usize,
        n: usize,
        k: usize,
        datatype: DataType,
        inputshape: (usize, usize, usize, usize),
        kernelshape: (usize, usize, usize, usize),
        stride: (usize, usize),
        padding: (usize, usize),
        parameters: HashMap<String, f32>,
/// ASIC instruction set
pub enum ASICInstruction {
    LoadMatrix {
        src_addr: usize,
        dst_pe: usize,
        rows: usize,
        cols: usize,
    StoreMatrix {
        src_pe: usize,
        dst_addr: usize,
        pe_id: usize,
        accumulate: bool,
    LoadConvKernel {
        kernel_height: usize,
        kernel_width: usize,
        input_channels: usize,
        output_channels: usize,
    LoadConvInput {
        height: usize,
        width: usize,
        channels: usize,
        stride_h: usize,
        stride_w: usize,
        padding_h: usize,
        padding_w: usize,
    Synchronize,
        opcode: u32,
        operands: Vec<u32>,
/// ASIC program (sequence of instructions)
pub struct ASICProgram {
    instructions: Vec<ASICInstruction>,
    metadata: ProgramMetadata,
impl ASICProgram {
    fn new() -> Self {
        Self {
            instructions: Vec::new(),
            metadata: ProgramMetadata::default(),
    fn add_instruction(&mut self, instruction: ASICInstruction) {
        self.instructions.push(instruction);
/// Program metadata
#[derive(Debug, Clone, Default)]
pub struct ProgramMetadata {
    pub estimated_cycles: u64,
    pub memory_footprint: usize,
    pub pe_utilization: f32,
/// Tiling configuration for matrix operations
#[derive(Debug)]
struct TilingConfig {
    tiles: Vec<MatMulTile>,
/// Matrix multiplication tile
struct MatMulTile {
    pe_id: usize,
    rows: usize,
    cols: usize,
    a_addr: usize,
    b_addr: usize,
    c_addr: usize,
    accumulate: bool,
/// ASIC memory manager
struct ASICMemoryManager {
    hierarchy: MemoryHierarchy,
    allocations: HashMap<usize, MemoryAllocation>,
    next_addr: usize,
impl ASICMemoryManager {
    fn new(hierarchy: &MemoryHierarchy) -> Self {
            hierarchy: hierarchy.clone(),
            allocations: HashMap::new(),
            next_addr: 0,
/// Memory allocation information
struct MemoryAllocation {
    addr: usize,
    size: usize,
    level: String,
/// Instruction cache
struct InstructionCache {
    cache: HashMap<usize, Vec<ASICInstruction>>,
impl InstructionCache {
    fn new(size: usize) -> Self {
            size,
            cache: HashMap::new(),
/// Performance counters
#[derive(Default)]
struct PerformanceCounters {
    instructions_executed: u64,
    execution_time: std::time::Duration,
    memory_accesses: u64,
    cache_hits: u64,
    cache_misses: u64,
impl PerformanceCounters {
        Self::default()
/// Runtime state
struct RuntimeState {
    active_programs: Vec<ASICProgram>,
    pe_states: Vec<PEState>,
impl RuntimeState {
            active_programs: Vec::new(),
            pe_states: Vec::new(),
/// Processing element state
struct PEState {
    busy: bool,
    current_instruction: Option<ASICInstruction>,
/// Performance statistics
pub struct PerformanceStats {
    pub instructions_executed: u64,
    pub total_execution_time: std::time::Duration,
    pub average_ipc: f64,
    pub memory_accesses: u64,
    pub cache_hits: u64,
    pub cache_misses: u64,
    pub power_consumption: f32,
// Implement Accelerator trait for CustomASIC
impl Accelerator for CustomASIC {
    fn accelerator_type(&self) -> AcceleratorType {
        AcceleratorType::ASIC
    fn capabilities(&self) -> &AcceleratorCapabilities {
        &self.capabilities
    fn initialize(&mut self) -> Result<()> {
        // Initialize the ASIC hardware
    fn is_available(&self) -> bool {
        true
    fn allocate(&self, size: usize) -> Result<DeviceBuffer> {
        // Simplified allocation
        let ptr = Box::into_raw(Box::new(vec![0u8; size])) as *mut u8;
        Ok(DeviceBuffer::new(ptr, size, 0))
    fn upload(&self, data: &ArrayView2<f32>) -> Result<DeviceBuffer> {
        let size = data.len() * std::mem::size_of::<f32>();
        let buffer = self.allocate(size)?;
        // Copy data (simplified)
        unsafe {
            std::ptr::copy_nonoverlapping(data.as_ptr() as *const u8, buffer.ptr, size);
        Ok(buffer)
    fn download(&self, buffer: &DeviceBuffer) -> Result<Array2<f32>> {
        let elements = buffer.size / std::mem::size_of::<f32>();
        let shape = (elements, 1); // Simplified
        let mut data = Array2::zeros(shape);
            std::ptr::copy_nonoverlapping(buffer.ptr as *const f32, data.as_mut_ptr(), elements);
        Ok(data)
    fn execute_kernel(
        &self,
        kernel: &dyn Kernel, inputs: &[&DeviceBuffer], _outputs: &mut [&mut DeviceBuffer],
    ) -> Result<()> {
        println!("Executing kernel: {} on Custom ASIC", kernel.name());
    fn synchronize(&self) -> Result<()> {
    fn memory_usage(&self) -> Result<MemoryInfo> {
        Ok(MemoryInfo {
            total: self.capabilities.total_memory,
            used: 0,
            available: self.capabilities.total_memory,
            reserved: 0,
    fn create_stream(&self) -> Result<ComputeStream> {
        Ok(ComputeStream {
            handle: std::ptr::null_mut(),
            id: 0,
            device_id: 0,
    fn profile_kernel(&self, kernel: &dyn Kernel) -> Result<ProfilingInfo> {
        Ok(ProfilingInfo {
            kernel_name: kernel.name().to_string(),
            execution_time_us: 100.0,
            memory_transfer_us: 10.0,
            occupancy: 0.85,
            memory_throughput: self.capabilities.memory_bandwidth,
            compute_throughput: self.capabilities.peak_tflops_fp32 * 1000.0,
#[cfg(test)]
mod tests {
    use super::*;
    fn create_test_asic_config() -> ASICConfig {
        ASICConfig {
            vendor: "TestCorp".to_string(),
            model: "NN-1000".to_string(),
            architecture_version: "v1.0".to_string(),
            processing_elements: 256,
            memory_hierarchy: MemoryHierarchy {
                sram_levels: vec![MemoryLevel {
                    name: "L1".to_string(),
                    size: 64 * 1024,
                    latency_cycles: 1,
                    bandwidth: 1000.0,
                    shared: false,
                }],
                external_memory: ExternalMemoryConfig {
                    memory_type: "HBM2".to_string(),
                    capacity: 32 * 1024 * 1024 * 1024,
                    bandwidth: 900.0,
                    latency_ns: 120.0,
                },
                cache_config: CacheConfig {
                    icache_size: 32 * 1024,
                    dcache_size: 64 * 1024,
                    line_size: 64,
                    associativity: 4,
            supported_datatypes: vec![DataType::Float32, DataType::Int8],
            native_operations: vec![NativeOperation::MatMul {
                tile_sizes: vec![(16, 16), (32, 32)],
                datatypes: vec![DataType::Float32],
            }],
            power_profile: PowerProfile {
                idle_power: 50.0,
                peak_power: 300.0,
                dynamic_power_per_op: 0.1,
                efficiency_tops_per_watt: 100.0,
            interconnect: InterconnectTopology {
                topology_type: TopologyType::Mesh2D {
                    width: 16,
                    height: 16,
                noc_config: None,
                bus_config: None,
    #[test]
    fn test_custom_asic_creation() {
        let config = create_test_asic_config();
        let asic = CustomASIC::new(_config).expect("Operation failed");
        assert_eq!(asic.accelerator_type(), AcceleratorType::ASIC);
        assert!(asic.is_available());
    fn test_asic_operation_compilation() {
        let operation = ASICOperation::MatMul {
            m: 128,
            n: 128,
            k: 128,
            datatype: DataType::Float32,
        let program = asic.compile_operation(&operation).expect("Operation failed");
        assert!(!program.instructions.is_empty());
    fn test_datatype_support() {
        let dt1 = DataType::FixedPoint {
            integer_bits: 8,
            fractional_bits: 8,
        let dt2 = DataType::Posit { nbits: 16, es: 1 };
        // Test custom datatypes
        match dt1 {
            DataType::FixedPoint {
                integer_bits,
                fractional_bits,
                assert_eq!(integer_bits, 8);
                assert_eq!(fractional_bits, 8);
            _ => unreachable!("Expected FixedPoint datatype"),
        match dt2 {
            DataType::Posit { nbits, es } => {
                assert_eq!(nbits, 16);
                assert_eq!(es, 1);
            _ => unreachable!("Expected Posit datatype"),