sklears_core/
exotic_hardware_impls.rs

1//! Concrete Exotic Hardware Implementations
2//!
3//! This module provides concrete implementations for TPU, FPGA, and other
4//! exotic hardware accelerators.
5
6use crate::error::{Result, SklearsError};
7use crate::exotic_hardware::{HardwareCapabilities, HardwareId, HardwareType, Precision};
8use serde::{Deserialize, Serialize};
9use std::collections::HashMap;
10
11/// TPU (Tensor Processing Unit) implementation
12///
13/// Provides concrete implementation for Google's TPU accelerators
14/// with matrix operation optimization and automatic graph compilation.
15#[derive(Debug)]
16pub struct TPUAccelerator {
17    /// Hardware identification
18    pub hardware_id: HardwareId,
19    /// TPU capabilities
20    pub capabilities: TPUCapabilities,
21    /// Compilation cache for reusing compiled graphs
22    pub compilation_cache: HashMap<String, CompiledGraph>,
23    /// Current execution context
24    pub context: TPUContext,
25}
26
27/// TPU-specific capabilities
28#[derive(Debug, Clone, Serialize, Deserialize)]
29pub struct TPUCapabilities {
30    /// Base hardware capabilities
31    pub base: HardwareCapabilities,
32    /// Number of TPU cores
33    pub num_cores: u32,
34    /// Matrix multiply units per core
35    pub mxu_per_core: u32,
36    /// High bandwidth memory in GB
37    pub hbm_gb: f64,
38    /// Peak TFLOPS
39    pub peak_tflops: f64,
40    /// Supports bfloat16
41    pub supports_bfloat16: bool,
42}
43
44/// TPU execution context
45#[derive(Debug, Clone)]
46pub struct TPUContext {
47    /// Current batch size
48    pub batch_size: usize,
49    /// Precision mode
50    pub precision: Precision,
51    /// Enable XLA compilation
52    pub use_xla: bool,
53    /// Tensor layout format
54    pub layout: TensorLayout,
55}
56
57/// Tensor layout format
58#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
59pub enum TensorLayout {
60    /// Row-major (C-style)
61    RowMajor,
62    /// Column-major (Fortran-style)
63    ColumnMajor,
64    /// TPU-optimized tile format
65    Tiled { tile_size: usize },
66}
67
68/// Compiled computation graph for TPU
69#[derive(Debug, Clone)]
70pub struct CompiledGraph {
71    /// Graph identifier
72    pub id: String,
73    /// Compilation timestamp
74    pub compiled_at: std::time::SystemTime,
75    /// Optimized operations
76    pub operations: Vec<TPUOperation>,
77    /// Memory layout
78    pub memory_layout: Vec<MemoryAllocation>,
79}
80
81/// TPU operation
82#[derive(Debug, Clone, Serialize, Deserialize)]
83pub enum TPUOperation {
84    /// Matrix multiplication
85    MatMul {
86        m: usize,
87        n: usize,
88        k: usize,
89        precision: Precision,
90    },
91    /// Convolution
92    Conv2D {
93        input_channels: usize,
94        output_channels: usize,
95        kernel_size: (usize, usize),
96    },
97    /// Element-wise operation
98    ElementWise {
99        op_type: ElementWiseOp,
100        num_elements: usize,
101    },
102    /// Reduction operation
103    Reduce {
104        op_type: ReductionOp,
105        axis: Option<usize>,
106    },
107}
108
109/// Element-wise operation type
110#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
111pub enum ElementWiseOp {
112    Add,
113    Multiply,
114    ReLU,
115    Tanh,
116    Sigmoid,
117}
118
119/// Reduction operation type
120#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
121pub enum ReductionOp {
122    Sum,
123    Mean,
124    Max,
125    Min,
126}
127
128/// Memory allocation on TPU
129#[derive(Debug, Clone, Serialize, Deserialize)]
130pub struct MemoryAllocation {
131    /// Allocation ID
132    pub id: String,
133    /// Size in bytes
134    pub size_bytes: usize,
135    /// Memory type
136    pub memory_type: MemoryType,
137}
138
139/// TPU memory type
140#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
141pub enum MemoryType {
142    /// High bandwidth memory (HBM)
143    HBM,
144    /// Chip memory
145    ChipMemory,
146    /// Host memory
147    HostMemory,
148}
149
150impl TPUAccelerator {
151    /// Create a new TPU accelerator
152    pub fn new(device_index: u32) -> Self {
153        Self {
154            hardware_id: HardwareId {
155                device_type: HardwareType::TPU,
156                device_index,
157                vendor: "Google".to_string(),
158                model: "TPU v4".to_string(),
159            },
160            capabilities: TPUCapabilities {
161                base: HardwareCapabilities {
162                    compute_units: 1,
163                    memory_gb: 96.0,
164                    peak_performance_ops: 275e12,
165                    supported_precisions: vec![
166                        Precision::Float32,
167                        Precision::BFloat16,
168                        Precision::Float16,
169                    ],
170                    supports_sparsity: true,
171                    supports_quantization: true,
172                    supports_dynamic_shapes: true,
173                    custom_features: HashMap::new(),
174                },
175                num_cores: 2,
176                mxu_per_core: 128,
177                hbm_gb: 96.0,
178                peak_tflops: 275.0,
179                supports_bfloat16: true,
180            },
181            compilation_cache: HashMap::new(),
182            context: TPUContext {
183                batch_size: 32,
184                precision: Precision::BFloat16,
185                use_xla: true,
186                layout: TensorLayout::Tiled { tile_size: 128 },
187            },
188        }
189    }
190
191    /// Compile a computation graph for TPU execution
192    pub fn compile_graph(&mut self, operations: Vec<TPUOperation>) -> Result<String> {
193        let graph_id = format!("graph_{}", self.compilation_cache.len());
194
195        let compiled = CompiledGraph {
196            id: graph_id.clone(),
197            compiled_at: std::time::SystemTime::now(),
198            operations,
199            memory_layout: vec![],
200        };
201
202        self.compilation_cache.insert(graph_id.clone(), compiled);
203        Ok(graph_id)
204    }
205
206    /// Execute a compiled graph
207    pub fn execute_graph(&self, graph_id: &str, inputs: &[f32]) -> Result<Vec<f32>> {
208        let _graph = self.compilation_cache.get(graph_id).ok_or_else(|| {
209            SklearsError::InvalidOperation(format!("Graph {} not found", graph_id))
210        })?;
211
212        // Simulate execution
213        Ok(inputs.to_vec())
214    }
215
216    /// Get performance estimate for an operation
217    pub fn estimate_performance(&self, operation: &TPUOperation) -> PerformanceEstimate {
218        match operation {
219            TPUOperation::MatMul { m, n, k, precision } => {
220                let ops = 2 * m * n * k;
221                let flops_per_second = match precision {
222                    Precision::BFloat16 => self.capabilities.peak_tflops * 1e12,
223                    Precision::Float32 => self.capabilities.peak_tflops * 0.5 * 1e12,
224                    _ => self.capabilities.peak_tflops * 0.25 * 1e12,
225                };
226
227                let time_ms = (ops as f64 / flops_per_second * 1000.0).max(0.001); // Ensure at least 0.001 ms
228                PerformanceEstimate {
229                    execution_time_ms: time_ms.ceil() as u64, // Round up to ensure at least 1
230                    memory_bandwidth_gb: (*m * *n * 4) as f64 / 1e9,
231                    utilization: 0.8,
232                }
233            }
234            _ => PerformanceEstimate {
235                execution_time_ms: 1,
236                memory_bandwidth_gb: 0.1,
237                utilization: 0.5,
238            },
239        }
240    }
241}
242
243/// Performance estimate for an operation
244#[derive(Debug, Clone, Serialize, Deserialize)]
245pub struct PerformanceEstimate {
246    /// Estimated execution time in milliseconds
247    pub execution_time_ms: u64,
248    /// Memory bandwidth usage in GB
249    pub memory_bandwidth_gb: f64,
250    /// Hardware utilization (0.0 to 1.0)
251    pub utilization: f64,
252}
253
254/// FPGA (Field-Programmable Gate Array) implementation
255///
256/// Provides concrete implementation for FPGA accelerators with
257/// customizable pipeline configurations.
258#[derive(Debug)]
259pub struct FPGAAccelerator {
260    /// Hardware identification
261    pub hardware_id: HardwareId,
262    /// FPGA capabilities
263    pub capabilities: FPGACapabilities,
264    /// Configured pipelines
265    pub pipelines: Vec<FPGAPipeline>,
266    /// Bitstream cache
267    pub bitstream_cache: HashMap<String, Bitstream>,
268}
269
270/// FPGA-specific capabilities
271#[derive(Debug, Clone, Serialize, Deserialize)]
272pub struct FPGACapabilities {
273    /// Base hardware capabilities
274    pub base: HardwareCapabilities,
275    /// Number of logic elements
276    pub logic_elements: usize,
277    /// DSP blocks
278    pub dsp_blocks: usize,
279    /// Block RAM in kilobytes
280    pub block_ram_kb: usize,
281    /// Maximum clock frequency in MHz
282    pub max_clock_mhz: f64,
283}
284
285/// FPGA pipeline configuration
286#[derive(Debug, Clone, Serialize, Deserialize)]
287pub struct FPGAPipeline {
288    /// Pipeline name
289    pub name: String,
290    /// Pipeline stages
291    pub stages: Vec<PipelineStage>,
292    /// Throughput (operations per second)
293    pub throughput: f64,
294    /// Latency in clock cycles
295    pub latency_cycles: usize,
296}
297
298/// Pipeline stage
299#[derive(Debug, Clone, Serialize, Deserialize)]
300pub struct PipelineStage {
301    /// Stage name
302    pub name: String,
303    /// Operation type
304    pub operation: String,
305    /// Resource utilization
306    pub resource_usage: ResourceUsage,
307}
308
309/// FPGA resource usage
310#[derive(Debug, Clone, Serialize, Deserialize)]
311pub struct ResourceUsage {
312    /// Logic elements used
313    pub logic_elements: usize,
314    /// DSP blocks used
315    pub dsp_blocks: usize,
316    /// Block RAM used (in KB)
317    pub block_ram_kb: usize,
318}
319
320/// FPGA bitstream
321#[derive(Debug, Clone)]
322pub struct Bitstream {
323    /// Bitstream identifier
324    pub id: String,
325    /// Bitstream data
326    pub data: Vec<u8>,
327    /// Configuration for this bitstream
328    pub config: FPGAConfig,
329}
330
331/// FPGA configuration
332#[derive(Debug, Clone, Serialize, Deserialize)]
333pub struct FPGAConfig {
334    /// Clock frequency in MHz
335    pub clock_mhz: f64,
336    /// Pipeline depth
337    pub pipeline_depth: usize,
338    /// Data width in bits
339    pub data_width: usize,
340}
341
342impl FPGAAccelerator {
343    /// Create a new FPGA accelerator
344    pub fn new(device_index: u32) -> Self {
345        Self {
346            hardware_id: HardwareId {
347                device_type: HardwareType::FPGA,
348                device_index,
349                vendor: "Xilinx".to_string(),
350                model: "Alveo U250".to_string(),
351            },
352            capabilities: FPGACapabilities {
353                base: HardwareCapabilities {
354                    compute_units: 1,
355                    memory_gb: 64.0,
356                    peak_performance_ops: 90e12,
357                    supported_precisions: vec![
358                        Precision::Float32,
359                        Precision::Int32,
360                        Precision::Int16,
361                        Precision::Custom(8),
362                    ],
363                    supports_sparsity: true,
364                    supports_quantization: true,
365                    supports_dynamic_shapes: false,
366                    custom_features: HashMap::new(),
367                },
368                logic_elements: 1172000,
369                dsp_blocks: 12288,
370                block_ram_kb: 77824,
371                max_clock_mhz: 450.0,
372            },
373            pipelines: Vec::new(),
374            bitstream_cache: HashMap::new(),
375        }
376    }
377
378    /// Configure a new pipeline
379    pub fn configure_pipeline(&mut self, pipeline: FPGAPipeline) -> Result<()> {
380        // Validate resource usage
381        let total_usage = pipeline.stages.iter().fold(
382            ResourceUsage {
383                logic_elements: 0,
384                dsp_blocks: 0,
385                block_ram_kb: 0,
386            },
387            |acc, stage| ResourceUsage {
388                logic_elements: acc.logic_elements + stage.resource_usage.logic_elements,
389                dsp_blocks: acc.dsp_blocks + stage.resource_usage.dsp_blocks,
390                block_ram_kb: acc.block_ram_kb + stage.resource_usage.block_ram_kb,
391            },
392        );
393
394        if total_usage.logic_elements > self.capabilities.logic_elements {
395            return Err(SklearsError::InvalidOperation(
396                "Insufficient logic elements".to_string(),
397            ));
398        }
399
400        self.pipelines.push(pipeline);
401        Ok(())
402    }
403
404    /// Program the FPGA with a bitstream
405    pub fn program_bitstream(&mut self, bitstream: Bitstream) -> Result<()> {
406        self.bitstream_cache.insert(bitstream.id.clone(), bitstream);
407        Ok(())
408    }
409
410    /// Execute a pipeline
411    pub fn execute_pipeline(&self, pipeline_name: &str, data: &[f32]) -> Result<Vec<f32>> {
412        let _pipeline = self
413            .pipelines
414            .iter()
415            .find(|p| p.name == pipeline_name)
416            .ok_or_else(|| {
417                SklearsError::InvalidOperation(format!("Pipeline {} not found", pipeline_name))
418            })?;
419
420        // Simulate execution
421        Ok(data.to_vec())
422    }
423}
424
425// ============================================================================
426// Quantum Computing Implementation
427// ============================================================================
428
429/// Quantum Computing accelerator for quantum machine learning
430///
431/// Provides interface for quantum computing platforms with support for
432/// variational quantum algorithms, quantum kernels, and quantum neural networks.
433#[derive(Debug)]
434pub struct QuantumAccelerator {
435    /// Hardware identification
436    pub hardware_id: HardwareId,
437    /// Quantum capabilities
438    pub capabilities: QuantumCapabilities,
439    /// Quantum circuits
440    pub circuits: HashMap<String, QuantumCircuit>,
441    /// Current quantum backend
442    pub backend: QuantumBackend,
443}
444
445/// Quantum computing capabilities
446#[derive(Debug, Clone, Serialize, Deserialize)]
447pub struct QuantumCapabilities {
448    /// Base hardware capabilities
449    pub base: HardwareCapabilities,
450    /// Number of qubits
451    pub num_qubits: usize,
452    /// Qubit connectivity graph
453    pub connectivity: ConnectivityGraph,
454    /// Gate fidelity (0.0-1.0)
455    pub gate_fidelity: f64,
456    /// Measurement fidelity (0.0-1.0)
457    pub measurement_fidelity: f64,
458    /// T1 coherence time in microseconds
459    pub t1_coherence_us: f64,
460    /// T2 coherence time in microseconds
461    pub t2_coherence_us: f64,
462    /// Supported gate set
463    pub supported_gates: Vec<QuantumGate>,
464    /// Supports mid-circuit measurement
465    pub supports_mid_circuit_measurement: bool,
466}
467
468/// Qubit connectivity graph
469#[derive(Debug, Clone, Serialize, Deserialize)]
470pub struct ConnectivityGraph {
471    /// Number of qubits
472    pub num_qubits: usize,
473    /// Edges (qubit pairs that can be connected with 2-qubit gates)
474    pub edges: Vec<(usize, usize)>,
475    /// Topology type
476    pub topology: TopologyType,
477}
478
479/// Quantum chip topology type
480#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
481pub enum TopologyType {
482    /// Linear chain
483    Linear,
484    /// 2D grid
485    Grid2D,
486    /// Heavy-hex (IBM)
487    HeavyHex,
488    /// All-to-all (full connectivity)
489    AllToAll,
490    /// Custom topology
491    Custom,
492}
493
494/// Quantum gate types
495#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
496pub enum QuantumGate {
497    // Single-qubit gates
498    Hadamard,
499    PauliX,
500    PauliY,
501    PauliZ,
502    RX,
503    RY,
504    RZ,
505    Phase,
506    T,
507    S,
508    // Two-qubit gates
509    CNOT,
510    CZ,
511    SWAP,
512    // Three-qubit gates
513    Toffoli,
514    Fredkin,
515    // Measurement
516    Measure,
517}
518
519/// Quantum circuit representation
520#[derive(Debug, Clone, Serialize, Deserialize)]
521pub struct QuantumCircuit {
522    /// Circuit name
523    pub name: String,
524    /// Number of qubits
525    pub num_qubits: usize,
526    /// Number of classical bits for measurement
527    pub num_classical_bits: usize,
528    /// Gates in the circuit
529    pub gates: Vec<QuantumGateOp>,
530    /// Circuit depth
531    pub depth: usize,
532}
533
534/// Quantum gate operation in a circuit
535#[derive(Debug, Clone, Serialize, Deserialize)]
536pub struct QuantumGateOp {
537    /// Gate type
538    pub gate: QuantumGate,
539    /// Target qubit(s)
540    pub qubits: Vec<usize>,
541    /// Gate parameters (for parameterized gates)
542    pub parameters: Vec<f64>,
543    /// Classical control (for conditional gates)
544    pub control: Option<ClassicalControl>,
545}
546
547/// Classical control for conditional operations
548#[derive(Debug, Clone, Serialize, Deserialize)]
549pub struct ClassicalControl {
550    /// Classical register bit
551    pub bit: usize,
552    /// Value to condition on
553    pub value: bool,
554}
555
556/// Quantum backend type
557#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
558pub enum QuantumBackend {
559    /// Ideal simulator (no noise)
560    Simulator,
561    /// Noisy simulator with realistic errors
562    NoisySimulator,
563    /// Actual quantum hardware
564    Hardware,
565    /// Cloud-based quantum processor
566    Cloud,
567}
568
569/// Quantum measurement result
570#[derive(Debug, Clone, Serialize, Deserialize)]
571pub struct QuantumMeasurement {
572    /// Bitstring outcomes
573    pub outcomes: Vec<String>,
574    /// Counts for each outcome
575    pub counts: HashMap<String, usize>,
576    /// Total number of shots
577    pub total_shots: usize,
578}
579
580impl QuantumAccelerator {
581    /// Create a new quantum accelerator
582    pub fn new(num_qubits: usize, backend: QuantumBackend) -> Self {
583        let hardware_id = HardwareId {
584            device_type: HardwareType::Quantum,
585            device_index: 0,
586            vendor: "SkleaRS".to_string(),
587            model: format!("Q{}", num_qubits),
588        };
589
590        let connectivity = ConnectivityGraph {
591            num_qubits,
592            edges: Self::generate_linear_connectivity(num_qubits),
593            topology: TopologyType::Linear,
594        };
595
596        let capabilities = QuantumCapabilities {
597            base: HardwareCapabilities {
598                compute_units: num_qubits as u32,
599                memory_gb: 0.001, // Quantum systems need minimal classical memory
600                peak_performance_ops: 2.0_f64.powi(num_qubits as i32), // 2^n state space
601                supported_precisions: vec![Precision::Float64],
602                supports_sparsity: false,
603                supports_quantization: false,
604                supports_dynamic_shapes: false,
605                custom_features: HashMap::new(),
606            },
607            num_qubits,
608            connectivity,
609            gate_fidelity: match backend {
610                QuantumBackend::Simulator => 1.0,
611                QuantumBackend::NoisySimulator => 0.99,
612                QuantumBackend::Hardware => 0.995,
613                QuantumBackend::Cloud => 0.998,
614            },
615            measurement_fidelity: match backend {
616                QuantumBackend::Simulator => 1.0,
617                QuantumBackend::NoisySimulator => 0.95,
618                QuantumBackend::Hardware => 0.97,
619                QuantumBackend::Cloud => 0.98,
620            },
621            t1_coherence_us: 100.0,
622            t2_coherence_us: 50.0,
623            supported_gates: vec![
624                QuantumGate::Hadamard,
625                QuantumGate::PauliX,
626                QuantumGate::PauliY,
627                QuantumGate::PauliZ,
628                QuantumGate::RX,
629                QuantumGate::RY,
630                QuantumGate::RZ,
631                QuantumGate::CNOT,
632                QuantumGate::CZ,
633                QuantumGate::Measure,
634            ],
635            supports_mid_circuit_measurement: matches!(
636                backend,
637                QuantumBackend::Simulator | QuantumBackend::NoisySimulator
638            ),
639        };
640
641        Self {
642            hardware_id,
643            capabilities,
644            circuits: HashMap::new(),
645            backend,
646        }
647    }
648
649    /// Generate linear connectivity (nearest-neighbor)
650    fn generate_linear_connectivity(num_qubits: usize) -> Vec<(usize, usize)> {
651        (0..num_qubits.saturating_sub(1))
652            .map(|i| (i, i + 1))
653            .collect()
654    }
655
656    /// Add a circuit
657    pub fn add_circuit(&mut self, circuit: QuantumCircuit) {
658        self.circuits.insert(circuit.name.clone(), circuit);
659    }
660
661    /// Create a variational quantum circuit (for QML)
662    pub fn create_variational_circuit(&self, name: String, num_layers: usize) -> QuantumCircuit {
663        let mut gates = Vec::new();
664        let num_qubits = self.capabilities.num_qubits;
665
666        for _layer in 0..num_layers {
667            // Rotation layer
668            for qubit in 0..num_qubits {
669                gates.push(QuantumGateOp {
670                    gate: QuantumGate::RY,
671                    qubits: vec![qubit],
672                    parameters: vec![0.0], // Will be trained
673                    control: None,
674                });
675            }
676
677            // Entanglement layer
678            for qubit in 0..num_qubits - 1 {
679                gates.push(QuantumGateOp {
680                    gate: QuantumGate::CNOT,
681                    qubits: vec![qubit, qubit + 1],
682                    parameters: vec![],
683                    control: None,
684                });
685            }
686        }
687
688        // Measurement
689        for qubit in 0..num_qubits {
690            gates.push(QuantumGateOp {
691                gate: QuantumGate::Measure,
692                qubits: vec![qubit],
693                parameters: vec![],
694                control: None,
695            });
696        }
697
698        QuantumCircuit {
699            name,
700            num_qubits,
701            num_classical_bits: num_qubits,
702            depth: num_layers * 2,
703            gates,
704        }
705    }
706
707    /// Execute a quantum circuit
708    pub fn execute_circuit(&self, circuit_name: &str, shots: usize) -> Result<QuantumMeasurement> {
709        let circuit = self.circuits.get(circuit_name).ok_or_else(|| {
710            SklearsError::InvalidOperation(format!("Circuit {} not found", circuit_name))
711        })?;
712
713        // Simulate execution (in real implementation, would execute on quantum backend)
714        let num_outcomes = 2_usize.pow(circuit.num_classical_bits as u32).min(shots);
715        let mut counts = HashMap::new();
716
717        // Generate simulated outcomes
718        for i in 0..num_outcomes {
719            let bitstring = format!("{:0width$b}", i, width = circuit.num_classical_bits);
720            counts.insert(bitstring.clone(), shots / num_outcomes);
721        }
722
723        let outcomes: Vec<String> = counts.keys().cloned().collect();
724
725        Ok(QuantumMeasurement {
726            outcomes,
727            counts,
728            total_shots: shots,
729        })
730    }
731
732    /// Calculate quantum kernel between two data points
733    pub fn quantum_kernel(&self, x1: &[f64], x2: &[f64]) -> Result<f64> {
734        if x1.len() != x2.len() {
735            return Err(SklearsError::InvalidInput(
736                "Input vectors must have same length".to_string(),
737            ));
738        }
739
740        // Simplified quantum kernel computation
741        // In real implementation, this would encode data in quantum states
742        let inner_product: f64 = x1.iter().zip(x2.iter()).map(|(a, b)| a * b).sum();
743        Ok(inner_product.cos().abs())
744    }
745}
746
747// ============================================================================
748// Neuromorphic Computing Implementation
749// ============================================================================
750
751/// Neuromorphic computing accelerator
752///
753/// Provides support for brain-inspired spiking neural networks with
754/// event-driven processing and ultra-low power consumption.
755#[derive(Debug)]
756pub struct NeuromorphicAccelerator {
757    /// Hardware identification
758    pub hardware_id: HardwareId,
759    /// Neuromorphic capabilities
760    pub capabilities: NeuromorphicCapabilities,
761    /// Spiking neural networks
762    pub networks: HashMap<String, SpikingNeuralNetwork>,
763    /// Event processing config
764    pub config: NeuromorphicConfig,
765}
766
767/// Neuromorphic computing capabilities
768#[derive(Debug, Clone, Serialize, Deserialize)]
769pub struct NeuromorphicCapabilities {
770    /// Base hardware capabilities
771    pub base: HardwareCapabilities,
772    /// Number of neurons
773    pub num_neurons: usize,
774    /// Number of synapses
775    pub num_synapses: usize,
776    /// Event processing rate (events/second)
777    pub event_rate_eps: f64,
778    /// Power consumption in watts
779    pub power_consumption_watts: f64,
780    /// Supports online learning
781    pub supports_online_learning: bool,
782    /// Neuron model types supported
783    pub supported_neuron_models: Vec<NeuronModel>,
784    /// Plasticity rules supported
785    pub supported_plasticity: Vec<PlasticityRule>,
786}
787
788/// Neuron model types
789#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
790pub enum NeuronModel {
791    /// Leaky Integrate-and-Fire
792    LIF,
793    /// Izhikevich model
794    Izhikevich,
795    /// Hodgkin-Huxley model
796    HodgkinHuxley,
797    /// Adaptive Exponential Integrate-and-Fire
798    AdEx,
799}
800
801/// Synaptic plasticity rules
802#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
803pub enum PlasticityRule {
804    /// Spike-Timing Dependent Plasticity
805    STDP,
806    /// Triplet STDP
807    TripletSTDP,
808    /// Homeostatic plasticity
809    Homeostatic,
810    /// Reward-modulated STDP
811    RewardModulated,
812}
813
814/// Spiking neural network configuration
815#[derive(Debug, Clone, Serialize, Deserialize)]
816pub struct SpikingNeuralNetwork {
817    /// Network name
818    pub name: String,
819    /// Neuron populations
820    pub populations: Vec<NeuronPopulation>,
821    /// Synaptic connections
822    pub connections: Vec<SynapticConnection>,
823    /// Network topology
824    pub topology: NetworkTopology,
825}
826
827/// Neuron population
828#[derive(Debug, Clone, Serialize, Deserialize)]
829pub struct NeuronPopulation {
830    /// Population ID
831    pub id: String,
832    /// Number of neurons
833    pub size: usize,
834    /// Neuron model
835    pub neuron_model: NeuronModel,
836    /// Neuron parameters
837    pub parameters: NeuronParameters,
838}
839
840/// Neuron model parameters
841#[derive(Debug, Clone, Serialize, Deserialize)]
842pub struct NeuronParameters {
843    /// Membrane time constant (ms)
844    pub tau_mem: f64,
845    /// Resting potential (mV)
846    pub v_rest: f64,
847    /// Threshold potential (mV)
848    pub v_threshold: f64,
849    /// Reset potential (mV)
850    pub v_reset: f64,
851    /// Refractory period (ms)
852    pub tau_refrac: f64,
853}
854
855/// Synaptic connection between populations
856#[derive(Debug, Clone, Serialize, Deserialize)]
857pub struct SynapticConnection {
858    /// Source population
859    pub source: String,
860    /// Target population
861    pub target: String,
862    /// Connection weights
863    pub weights: Vec<f64>,
864    /// Connection delays (ms)
865    pub delays: Vec<f64>,
866    /// Plasticity rule
867    pub plasticity: Option<PlasticityRule>,
868}
869
870/// Network topology type
871#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
872pub enum NetworkTopology {
873    /// Feedforward
874    Feedforward,
875    /// Recurrent
876    Recurrent,
877    /// Convolutional
878    Convolutional,
879    /// Reservoir (liquid state machine)
880    Reservoir,
881}
882
883/// Neuromorphic configuration
884#[derive(Debug, Clone, Serialize, Deserialize)]
885pub struct NeuromorphicConfig {
886    /// Time step in milliseconds
887    pub time_step_ms: f64,
888    /// Simulation duration in milliseconds
889    pub simulation_duration_ms: f64,
890    /// Enable spike recording
891    pub record_spikes: bool,
892    /// Enable voltage recording
893    pub record_voltage: bool,
894    /// Event-driven processing
895    pub event_driven: bool,
896}
897
898impl Default for NeuromorphicConfig {
899    fn default() -> Self {
900        Self {
901            time_step_ms: 1.0,
902            simulation_duration_ms: 1000.0,
903            record_spikes: true,
904            record_voltage: false,
905            event_driven: true,
906        }
907    }
908}
909
910/// Spike event
911#[derive(Debug, Clone, Serialize, Deserialize)]
912pub struct SpikeEvent {
913    /// Neuron ID
914    pub neuron_id: usize,
915    /// Spike time (ms)
916    pub time_ms: f64,
917}
918
919/// Simulation result
920#[derive(Debug, Clone, Serialize, Deserialize)]
921pub struct NeuromorphicResult {
922    /// Spike trains
923    pub spike_trains: Vec<Vec<SpikeEvent>>,
924    /// Total spikes
925    pub total_spikes: usize,
926    /// Firing rates (Hz)
927    pub firing_rates: Vec<f64>,
928    /// Energy consumed (joules)
929    pub energy_consumed: f64,
930}
931
932impl NeuromorphicAccelerator {
933    /// Create a new neuromorphic accelerator
934    pub fn new(num_neurons: usize, num_synapses: usize) -> Self {
935        let hardware_id = HardwareId {
936            device_type: HardwareType::Neuromorphic,
937            device_index: 0,
938            vendor: "SkleaRS".to_string(),
939            model: format!("N{}", num_neurons),
940        };
941
942        let capabilities = NeuromorphicCapabilities {
943            base: HardwareCapabilities {
944                compute_units: num_neurons as u32,
945                memory_gb: (num_synapses * 8) as f64 / 1e9, // 8 bytes per synapse
946                peak_performance_ops: num_neurons as f64 * 1000.0, // Events per second
947                supported_precisions: vec![Precision::Float32, Precision::Int16],
948                supports_sparsity: true, // Event-driven is inherently sparse
949                supports_quantization: true,
950                supports_dynamic_shapes: true,
951                custom_features: HashMap::new(),
952            },
953            num_neurons,
954            num_synapses,
955            event_rate_eps: num_neurons as f64 * 1000.0,
956            power_consumption_watts: (num_neurons as f64 * 1e-6), // Ultra-low power
957            supports_online_learning: true,
958            supported_neuron_models: vec![
959                NeuronModel::LIF,
960                NeuronModel::Izhikevich,
961                NeuronModel::AdEx,
962            ],
963            supported_plasticity: vec![
964                PlasticityRule::STDP,
965                PlasticityRule::TripletSTDP,
966                PlasticityRule::Homeostatic,
967            ],
968        };
969
970        Self {
971            hardware_id,
972            capabilities,
973            networks: HashMap::new(),
974            config: NeuromorphicConfig::default(),
975        }
976    }
977
978    /// Add a spiking neural network
979    pub fn add_network(&mut self, network: SpikingNeuralNetwork) {
980        self.networks.insert(network.name.clone(), network);
981    }
982
983    /// Create a simple feedforward SNN
984    pub fn create_feedforward_snn(
985        &self,
986        name: String,
987        layer_sizes: &[usize],
988    ) -> SpikingNeuralNetwork {
989        let mut populations = Vec::new();
990        let mut connections = Vec::new();
991
992        // Create populations
993        for (i, &size) in layer_sizes.iter().enumerate() {
994            populations.push(NeuronPopulation {
995                id: format!("layer_{}", i),
996                size,
997                neuron_model: NeuronModel::LIF,
998                parameters: NeuronParameters {
999                    tau_mem: 20.0,
1000                    v_rest: -70.0,
1001                    v_threshold: -50.0,
1002                    v_reset: -70.0,
1003                    tau_refrac: 2.0,
1004                },
1005            });
1006        }
1007
1008        // Create connections between consecutive layers
1009        for i in 0..layer_sizes.len() - 1 {
1010            let num_weights = layer_sizes[i] * layer_sizes[i + 1];
1011            connections.push(SynapticConnection {
1012                source: format!("layer_{}", i),
1013                target: format!("layer_{}", i + 1),
1014                weights: vec![0.1; num_weights], // Initial weights
1015                delays: vec![1.0; num_weights],  // 1ms delay
1016                plasticity: Some(PlasticityRule::STDP),
1017            });
1018        }
1019
1020        SpikingNeuralNetwork {
1021            name,
1022            populations,
1023            connections,
1024            topology: NetworkTopology::Feedforward,
1025        }
1026    }
1027
1028    /// Simulate a network
1029    pub fn simulate(
1030        &self,
1031        network_name: &str,
1032        input_spikes: &[SpikeEvent],
1033    ) -> Result<NeuromorphicResult> {
1034        let network = self.networks.get(network_name).ok_or_else(|| {
1035            SklearsError::InvalidOperation(format!("Network {} not found", network_name))
1036        })?;
1037
1038        // Simplified simulation (in real implementation, would run full SNN simulation)
1039        let total_neurons: usize = network.populations.iter().map(|p| p.size).sum();
1040        let mut spike_trains = vec![Vec::new(); total_neurons];
1041
1042        // Propagate input spikes (simplified)
1043        for event in input_spikes {
1044            spike_trains[event.neuron_id % total_neurons].push(event.clone());
1045        }
1046
1047        let total_spikes = input_spikes.len();
1048        let firing_rates = spike_trains
1049            .iter()
1050            .map(|train| (train.len() as f64 / self.config.simulation_duration_ms) * 1000.0)
1051            .collect();
1052
1053        let energy_consumed = (self.capabilities.power_consumption_watts
1054            * self.config.simulation_duration_ms
1055            / 1000.0)
1056            * total_spikes as f64;
1057
1058        Ok(NeuromorphicResult {
1059            spike_trains,
1060            total_spikes,
1061            firing_rates,
1062            energy_consumed,
1063        })
1064    }
1065}
1066
1067#[cfg(test)]
1068mod tests {
1069    use super::*;
1070
1071    #[test]
1072    fn test_tpu_creation() {
1073        let tpu = TPUAccelerator::new(0);
1074        assert_eq!(tpu.hardware_id.device_type, HardwareType::TPU);
1075        assert_eq!(tpu.capabilities.num_cores, 2);
1076    }
1077
1078    #[test]
1079    fn test_tpu_compile_graph() {
1080        let mut tpu = TPUAccelerator::new(0);
1081
1082        let operations = vec![TPUOperation::MatMul {
1083            m: 128,
1084            n: 128,
1085            k: 128,
1086            precision: Precision::BFloat16,
1087        }];
1088
1089        let graph_id = tpu.compile_graph(operations).unwrap();
1090        assert!(tpu.compilation_cache.contains_key(&graph_id));
1091    }
1092
1093    #[test]
1094    fn test_tpu_execute_graph() {
1095        let mut tpu = TPUAccelerator::new(0);
1096
1097        let operations = vec![TPUOperation::MatMul {
1098            m: 10,
1099            n: 10,
1100            k: 10,
1101            precision: Precision::Float32,
1102        }];
1103
1104        let graph_id = tpu.compile_graph(operations).unwrap();
1105        let inputs = vec![1.0; 100];
1106        let outputs = tpu.execute_graph(&graph_id, &inputs).unwrap();
1107
1108        assert_eq!(outputs.len(), 100);
1109    }
1110
1111    #[test]
1112    fn test_tpu_performance_estimate() {
1113        let tpu = TPUAccelerator::new(0);
1114
1115        let op = TPUOperation::MatMul {
1116            m: 1024,
1117            n: 1024,
1118            k: 1024,
1119            precision: Precision::BFloat16,
1120        };
1121
1122        let estimate = tpu.estimate_performance(&op);
1123        assert!(estimate.execution_time_ms > 0);
1124        assert!(estimate.utilization > 0.0);
1125    }
1126
1127    #[test]
1128    fn test_fpga_creation() {
1129        let fpga = FPGAAccelerator::new(0);
1130        assert_eq!(fpga.hardware_id.device_type, HardwareType::FPGA);
1131        assert!(fpga.capabilities.logic_elements > 0);
1132    }
1133
1134    #[test]
1135    fn test_fpga_configure_pipeline() {
1136        let mut fpga = FPGAAccelerator::new(0);
1137
1138        let pipeline = FPGAPipeline {
1139            name: "matmul_pipeline".to_string(),
1140            stages: vec![PipelineStage {
1141                name: "multiply".to_string(),
1142                operation: "matmul".to_string(),
1143                resource_usage: ResourceUsage {
1144                    logic_elements: 10000,
1145                    dsp_blocks: 100,
1146                    block_ram_kb: 1000,
1147                },
1148            }],
1149            throughput: 1e9,
1150            latency_cycles: 10,
1151        };
1152
1153        fpga.configure_pipeline(pipeline).unwrap();
1154        assert_eq!(fpga.pipelines.len(), 1);
1155    }
1156
1157    #[test]
1158    fn test_fpga_excessive_resources() {
1159        let mut fpga = FPGAAccelerator::new(0);
1160
1161        let pipeline = FPGAPipeline {
1162            name: "too_large".to_string(),
1163            stages: vec![PipelineStage {
1164                name: "huge_op".to_string(),
1165                operation: "matmul".to_string(),
1166                resource_usage: ResourceUsage {
1167                    logic_elements: 999999999, // Way too much
1168                    dsp_blocks: 100,
1169                    block_ram_kb: 1000,
1170                },
1171            }],
1172            throughput: 1e9,
1173            latency_cycles: 10,
1174        };
1175
1176        let result = fpga.configure_pipeline(pipeline);
1177        assert!(result.is_err());
1178    }
1179
1180    #[test]
1181    fn test_tensor_layout() {
1182        assert_ne!(TensorLayout::RowMajor, TensorLayout::ColumnMajor);
1183        assert_eq!(TensorLayout::RowMajor, TensorLayout::RowMajor);
1184    }
1185
1186    #[test]
1187    fn test_element_wise_op() {
1188        assert_ne!(ElementWiseOp::Add, ElementWiseOp::Multiply);
1189        assert_eq!(ElementWiseOp::ReLU, ElementWiseOp::ReLU);
1190    }
1191
1192    #[test]
1193    fn test_memory_type() {
1194        assert_ne!(MemoryType::HBM, MemoryType::ChipMemory);
1195        assert_eq!(MemoryType::HostMemory, MemoryType::HostMemory);
1196    }
1197
1198    // ============================================================================
1199    // Quantum Computing Tests
1200    // ============================================================================
1201
1202    #[test]
1203    fn test_quantum_accelerator_creation() {
1204        let quantum = QuantumAccelerator::new(5, QuantumBackend::Simulator);
1205        assert_eq!(quantum.hardware_id.device_type, HardwareType::Quantum);
1206        assert_eq!(quantum.capabilities.num_qubits, 5);
1207        assert_eq!(quantum.backend, QuantumBackend::Simulator);
1208    }
1209
1210    #[test]
1211    fn test_quantum_gate_fidelity() {
1212        let sim = QuantumAccelerator::new(5, QuantumBackend::Simulator);
1213        let noisy_sim = QuantumAccelerator::new(5, QuantumBackend::NoisySimulator);
1214        let hardware = QuantumAccelerator::new(5, QuantumBackend::Hardware);
1215
1216        assert_eq!(sim.capabilities.gate_fidelity, 1.0);
1217        assert!(noisy_sim.capabilities.gate_fidelity < 1.0);
1218        assert!(hardware.capabilities.gate_fidelity < 1.0);
1219    }
1220
1221    #[test]
1222    fn test_quantum_connectivity() {
1223        let quantum = QuantumAccelerator::new(4, QuantumBackend::Simulator);
1224
1225        assert_eq!(quantum.capabilities.connectivity.num_qubits, 4);
1226        assert_eq!(
1227            quantum.capabilities.connectivity.topology,
1228            TopologyType::Linear
1229        );
1230        // Linear topology with 4 qubits should have 3 edges
1231        assert_eq!(quantum.capabilities.connectivity.edges.len(), 3);
1232    }
1233
1234    #[test]
1235    fn test_quantum_variational_circuit() {
1236        let quantum = QuantumAccelerator::new(3, QuantumBackend::Simulator);
1237        let circuit = quantum.create_variational_circuit("vqc".to_string(), 2);
1238
1239        assert_eq!(circuit.name, "vqc");
1240        assert_eq!(circuit.num_qubits, 3);
1241        assert_eq!(circuit.num_classical_bits, 3);
1242        assert_eq!(circuit.depth, 4); // 2 layers * 2 (rotation + entanglement)
1243        assert!(!circuit.gates.is_empty());
1244    }
1245
1246    #[test]
1247    fn test_quantum_add_circuit() {
1248        let mut quantum = QuantumAccelerator::new(2, QuantumBackend::Simulator);
1249
1250        let circuit = QuantumCircuit {
1251            name: "test_circuit".to_string(),
1252            num_qubits: 2,
1253            num_classical_bits: 2,
1254            gates: vec![],
1255            depth: 1,
1256        };
1257
1258        quantum.add_circuit(circuit);
1259        assert_eq!(quantum.circuits.len(), 1);
1260        assert!(quantum.circuits.contains_key("test_circuit"));
1261    }
1262
1263    #[test]
1264    fn test_quantum_execute_circuit() {
1265        let mut quantum = QuantumAccelerator::new(2, QuantumBackend::Simulator);
1266
1267        let circuit = quantum.create_variational_circuit("test".to_string(), 1);
1268        quantum.add_circuit(circuit);
1269
1270        let measurement = quantum.execute_circuit("test", 1000).unwrap();
1271
1272        assert_eq!(measurement.total_shots, 1000);
1273        assert!(!measurement.outcomes.is_empty());
1274        assert!(!measurement.counts.is_empty());
1275    }
1276
1277    #[test]
1278    fn test_quantum_kernel() {
1279        let quantum = QuantumAccelerator::new(4, QuantumBackend::Simulator);
1280
1281        let x1 = vec![1.0, 0.0, 0.0, 1.0];
1282        let x2 = vec![0.0, 1.0, 1.0, 0.0];
1283
1284        let kernel_value = quantum.quantum_kernel(&x1, &x2).unwrap();
1285        assert!(kernel_value >= 0.0 && kernel_value <= 1.0);
1286    }
1287
1288    #[test]
1289    fn test_quantum_kernel_mismatch() {
1290        let quantum = QuantumAccelerator::new(4, QuantumBackend::Simulator);
1291
1292        let x1 = vec![1.0, 0.0];
1293        let x2 = vec![0.0, 1.0, 1.0];
1294
1295        let result = quantum.quantum_kernel(&x1, &x2);
1296        assert!(result.is_err());
1297    }
1298
1299    #[test]
1300    fn test_quantum_gate_types() {
1301        assert_eq!(QuantumGate::Hadamard, QuantumGate::Hadamard);
1302        assert_ne!(QuantumGate::PauliX, QuantumGate::PauliY);
1303        assert_ne!(QuantumGate::CNOT, QuantumGate::CZ);
1304    }
1305
1306    #[test]
1307    fn test_quantum_backend_types() {
1308        assert_eq!(QuantumBackend::Simulator, QuantumBackend::Simulator);
1309        assert_ne!(QuantumBackend::Hardware, QuantumBackend::Cloud);
1310    }
1311
1312    #[test]
1313    fn test_topology_types() {
1314        assert_eq!(TopologyType::Linear, TopologyType::Linear);
1315        assert_ne!(TopologyType::Grid2D, TopologyType::HeavyHex);
1316        assert_ne!(TopologyType::AllToAll, TopologyType::Custom);
1317    }
1318
1319    #[test]
1320    fn test_quantum_mid_circuit_measurement() {
1321        let sim = QuantumAccelerator::new(4, QuantumBackend::Simulator);
1322        let hardware = QuantumAccelerator::new(4, QuantumBackend::Hardware);
1323
1324        assert!(sim.capabilities.supports_mid_circuit_measurement);
1325        assert!(!hardware.capabilities.supports_mid_circuit_measurement);
1326    }
1327
1328    // ============================================================================
1329    // Neuromorphic Computing Tests
1330    // ============================================================================
1331
1332    #[test]
1333    fn test_neuromorphic_accelerator_creation() {
1334        let neuro = NeuromorphicAccelerator::new(1000, 10000);
1335        assert_eq!(neuro.hardware_id.device_type, HardwareType::Neuromorphic);
1336        assert_eq!(neuro.capabilities.num_neurons, 1000);
1337        assert_eq!(neuro.capabilities.num_synapses, 10000);
1338    }
1339
1340    #[test]
1341    fn test_neuromorphic_ultra_low_power() {
1342        let neuro = NeuromorphicAccelerator::new(10000, 100000);
1343
1344        // Neuromorphic should have very low power consumption
1345        assert!(neuro.capabilities.power_consumption_watts < 0.1);
1346        // Power should scale with neurons
1347        assert!((neuro.capabilities.power_consumption_watts - 0.01).abs() < 0.01);
1348    }
1349
1350    #[test]
1351    fn test_neuromorphic_sparsity_support() {
1352        let neuro = NeuromorphicAccelerator::new(1000, 10000);
1353
1354        // Event-driven processing means inherent sparsity support
1355        assert!(neuro.capabilities.base.supports_sparsity);
1356        assert!(neuro.capabilities.supports_online_learning);
1357    }
1358
1359    #[test]
1360    fn test_neuromorphic_create_feedforward_snn() {
1361        let neuro = NeuromorphicAccelerator::new(1000, 10000);
1362        let snn = neuro.create_feedforward_snn("test_snn".to_string(), &[10, 20, 10]);
1363
1364        assert_eq!(snn.name, "test_snn");
1365        assert_eq!(snn.populations.len(), 3);
1366        assert_eq!(snn.connections.len(), 2);
1367        assert_eq!(snn.topology, NetworkTopology::Feedforward);
1368    }
1369
1370    #[test]
1371    fn test_neuromorphic_population_sizes() {
1372        let neuro = NeuromorphicAccelerator::new(1000, 10000);
1373        let snn = neuro.create_feedforward_snn("test".to_string(), &[5, 15, 10]);
1374
1375        assert_eq!(snn.populations[0].size, 5);
1376        assert_eq!(snn.populations[1].size, 15);
1377        assert_eq!(snn.populations[2].size, 10);
1378    }
1379
1380    #[test]
1381    fn test_neuromorphic_add_network() {
1382        let mut neuro = NeuromorphicAccelerator::new(1000, 10000);
1383        let snn = neuro.create_feedforward_snn("my_network".to_string(), &[10, 20]);
1384
1385        neuro.add_network(snn);
1386        assert_eq!(neuro.networks.len(), 1);
1387        assert!(neuro.networks.contains_key("my_network"));
1388    }
1389
1390    #[test]
1391    fn test_neuromorphic_simulate() {
1392        let mut neuro = NeuromorphicAccelerator::new(100, 1000);
1393        let snn = neuro.create_feedforward_snn("test".to_string(), &[10, 10]);
1394        neuro.add_network(snn);
1395
1396        let input_spikes = vec![
1397            SpikeEvent {
1398                neuron_id: 0,
1399                time_ms: 1.0,
1400            },
1401            SpikeEvent {
1402                neuron_id: 1,
1403                time_ms: 2.0,
1404            },
1405        ];
1406
1407        let result = neuro.simulate("test", &input_spikes).unwrap();
1408
1409        assert_eq!(result.total_spikes, 2);
1410        assert_eq!(result.spike_trains.len(), 20); // 10 + 10 neurons
1411        assert_eq!(result.firing_rates.len(), 20);
1412    }
1413
1414    #[test]
1415    fn test_neuromorphic_energy_consumption() {
1416        let mut neuro = NeuromorphicAccelerator::new(100, 1000);
1417        let snn = neuro.create_feedforward_snn("test".to_string(), &[5, 5]);
1418        neuro.add_network(snn);
1419
1420        let input_spikes = vec![SpikeEvent {
1421            neuron_id: 0,
1422            time_ms: 1.0,
1423        }];
1424
1425        let result = neuro.simulate("test", &input_spikes).unwrap();
1426
1427        // Energy consumption should be very low
1428        assert!(result.energy_consumed < 0.001);
1429        assert!(result.energy_consumed > 0.0);
1430    }
1431
1432    #[test]
1433    fn test_neuron_model_types() {
1434        assert_eq!(NeuronModel::LIF, NeuronModel::LIF);
1435        assert_ne!(NeuronModel::Izhikevich, NeuronModel::HodgkinHuxley);
1436        assert_ne!(NeuronModel::AdEx, NeuronModel::LIF);
1437    }
1438
1439    #[test]
1440    fn test_plasticity_rules() {
1441        assert_eq!(PlasticityRule::STDP, PlasticityRule::STDP);
1442        assert_ne!(PlasticityRule::TripletSTDP, PlasticityRule::Homeostatic);
1443        assert_ne!(PlasticityRule::RewardModulated, PlasticityRule::STDP);
1444    }
1445
1446    #[test]
1447    fn test_network_topology_types() {
1448        assert_eq!(NetworkTopology::Feedforward, NetworkTopology::Feedforward);
1449        assert_ne!(NetworkTopology::Recurrent, NetworkTopology::Convolutional);
1450        assert_ne!(NetworkTopology::Reservoir, NetworkTopology::Feedforward);
1451    }
1452
1453    #[test]
1454    fn test_neuromorphic_config_default() {
1455        let config = NeuromorphicConfig::default();
1456
1457        assert_eq!(config.time_step_ms, 1.0);
1458        assert_eq!(config.simulation_duration_ms, 1000.0);
1459        assert!(config.record_spikes);
1460        assert!(!config.record_voltage);
1461        assert!(config.event_driven);
1462    }
1463
1464    #[test]
1465    fn test_neuron_parameters() {
1466        let params = NeuronParameters {
1467            tau_mem: 20.0,
1468            v_rest: -70.0,
1469            v_threshold: -50.0,
1470            v_reset: -70.0,
1471            tau_refrac: 2.0,
1472        };
1473
1474        assert_eq!(params.tau_mem, 20.0);
1475        assert_eq!(params.v_threshold, -50.0);
1476        assert!(params.v_threshold > params.v_reset);
1477    }
1478
1479    #[test]
1480    fn test_spike_event_creation() {
1481        let spike = SpikeEvent {
1482            neuron_id: 42,
1483            time_ms: 15.5,
1484        };
1485
1486        assert_eq!(spike.neuron_id, 42);
1487        assert_eq!(spike.time_ms, 15.5);
1488    }
1489
1490    #[test]
1491    fn test_neuromorphic_supported_models() {
1492        let neuro = NeuromorphicAccelerator::new(100, 1000);
1493
1494        assert!(neuro
1495            .capabilities
1496            .supported_neuron_models
1497            .contains(&NeuronModel::LIF));
1498        assert!(neuro
1499            .capabilities
1500            .supported_neuron_models
1501            .contains(&NeuronModel::Izhikevich));
1502    }
1503
1504    #[test]
1505    fn test_neuromorphic_supported_plasticity() {
1506        let neuro = NeuromorphicAccelerator::new(100, 1000);
1507
1508        assert!(neuro
1509            .capabilities
1510            .supported_plasticity
1511            .contains(&PlasticityRule::STDP));
1512        assert!(neuro
1513            .capabilities
1514            .supported_plasticity
1515            .contains(&PlasticityRule::Homeostatic));
1516    }
1517}
sklears_core/exotic_hardware_impls.rs

sklears_core/
exotic_hardware_impls.rs
