Deep Neural Architecture

// SonexAI Core Engine - Neural Network Inference
// Version 1.0.58 - Production Build

#include "sonex/core/neural_engine.hpp"
#include "sonex/vision/frame_processor.hpp"
#include "sonex/decision/policy_network.hpp"
#include <cuda_runtime.h>
#include <tensorrt/NvInfer.h>

namespace sonex {
namespace core {

class NeuralEngine {
public:
    struct InferenceResult {
        ActionVector discrete_actions;   // Movement, abilities, weapons
        MouseDelta continuous_actions;   // dx, dy mouse movement
        float value_estimate;            // Expected reward
        float confidence;                // Action confidence score
        uint64_t latency_us;            // Inference time in microseconds
    };

    NeuralEngine(const EngineConfig& config) 
        : m_config(config)
        , m_vision_encoder(std::make_unique<VisionEncoder>(config.vision))
        , m_temporal_encoder(std::make_unique<TemporalEncoder>(config.temporal))
        , m_policy_network(std::make_unique<PolicyNetwork>(config.policy))
        , m_value_network(std::make_unique<ValueNetwork>(config.value))
    {
        // Initialize CUDA context and TensorRT engines
        CUDA_CHECK(cudaSetDevice(config.gpu_id));
        initializeTensorRTEngines();
        
        // Pre-allocate GPU memory for zero-copy inference
        allocateInferenceBuffers();
        
        // Warm up the pipeline
        warmupInference(config.warmup_iterations);
        
        SONEX_LOG_INFO("NeuralEngine initialized on GPU {}", config.gpu_id);
    }

    InferenceResult infer(const GameFrame& frame, const GameState& state) {
        auto start = std::chrono::high_resolution_clock::now();
        
        // Stage 1: Vision encoding (GPU)
        // Extract spatial features from game frame using ResNet-50 backbone
        auto vision_features = m_vision_encoder->encode(frame);
        
        // Stage 2: Temporal encoding (GPU)
        // Process sequence with LSTM to maintain game state memory
        auto temporal_features = m_temporal_encoder->encode(
            vision_features, 
            state,
            m_hidden_state
        );
        
        // Stage 3: Policy inference (GPU)
        // Compute action probability distribution
        auto [action_logits, mouse_params] = m_policy_network->forward(
            temporal_features
        );
        
        // Stage 4: Value estimation (GPU)
        // Estimate expected cumulative reward
        float value = m_value_network->forward(temporal_features);
        
        // Stage 5: Action sampling
        // Sample actions from probability distributions
        auto actions = sampleActions(action_logits, mouse_params);
        
        auto end = std::chrono::high_resolution_clock::now();
        auto latency = std::chrono::duration_cast<std::chrono::microseconds>(
            end - start
        ).count();
        
        return InferenceResult{
            .discrete_actions = actions.discrete,
            .continuous_actions = actions.continuous,
            .value_estimate = value,
            .confidence = computeConfidence(action_logits),
            .latency_us = latency
        };
    }

private:
    void initializeTensorRTEngines() {
        // Load optimized TensorRT engines for each network
        m_trt_vision = loadEngine("models/vision_fp16.engine");
        m_trt_temporal = loadEngine("models/temporal_fp16.engine");
        m_trt_policy = loadEngine("models/policy_fp16.engine");
        m_trt_value = loadEngine("models/value_fp16.engine");
        
        // Create execution contexts
        m_ctx_vision = m_trt_vision->createExecutionContext();
        m_ctx_temporal = m_trt_temporal->createExecutionContext();
        m_ctx_policy = m_trt_policy->createExecutionContext();
        m_ctx_value = m_trt_value->createExecutionContext();
    }
    
    ActionSample sampleActions(
        const Tensor& logits, 
        const Tensor& mouse_params
    ) {
        // Apply temperature scaling for controlled randomness
        auto scaled_logits = logits / m_config.temperature;
        
        // Categorical sampling for discrete actions
        auto discrete = torch::multinomial(
            torch::softmax(scaled_logits, -1), 
            1
        ).squeeze();
        
        // Gaussian sampling for continuous mouse movement
        auto [mu, sigma] = mouse_params.chunk(2, -1);
        auto continuous = mu + sigma * torch::randn_like(sigma);
        
        // Apply action bounds
        continuous = torch::clamp(continuous, -1.0f, 1.0f);
        
        return ActionSample{discrete, continuous};
    }
    
    EngineConfig m_config;
    std::unique_ptr<VisionEncoder> m_vision_encoder;
    std::unique_ptr<TemporalEncoder> m_temporal_encoder;
    std::unique_ptr<PolicyNetwork> m_policy_network;
    std::unique_ptr<ValueNetwork> m_value_network;
    
    // TensorRT engines and contexts
    nvinfer1::ICudaEngine* m_trt_vision;
    nvinfer1::ICudaEngine* m_trt_temporal;
    nvinfer1::ICudaEngine* m_trt_policy;
    nvinfer1::ICudaEngine* m_trt_value;
    
    nvinfer1::IExecutionContext* m_ctx_vision;
    nvinfer1::IExecutionContext* m_ctx_temporal;
    nvinfer1::IExecutionContext* m_ctx_policy;
    nvinfer1::IExecutionContext* m_ctx_value;
    
    // Recurrent hidden state
    Tensor m_hidden_state;
};

} // namespace core
} // namespace sonex