AI Inference Engine Ontology

FLOW ALGEBRA
AI Inference Engine Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/artificial-intelligence-infrastructure/ai-inference-engine-ontology.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "AIInferenceEngineOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := ArtificialIntelligenceInfrastructure
ONTOLOGY := AIInferenceEngineOntology

# ---------- PRIMARY ENTITY ----------
ENTITY AIInferenceEngine {
    definition:
        "A software execution system that loads a trained artificial
         intelligence model, prepares input data, executes the model's
         computational graph or operators on available hardware and produces
         predictions, generated outputs or other inference results.";

    objectives: [
        ModelInference,
        LowLatencyExecution,
        HighThroughput,
        HardwareAcceleration,
        EfficientMemoryUse,
        ReliableServing,
        ScalablePrediction
    ];
}

# ---------- CORE ENTITIES ----------
InferenceEntity := {
    Model,
    ModelVersion,
    InferenceRequest,
    InputTensor,
    OutputTensor,
    Prediction,
    InferenceEngine,
    ModelServer
};

ExecutionEntity := {
    Operator,
    Kernel,
    ExecutionGraph,
    ExecutionContext,
    Device,
    MemoryBuffer,
    Batch
};

ServingEntity := {
    Endpoint,
    RequestQueue,
    Scheduler,
    LoadBalancer,
    Replica,
    Cache,
    HealthCheck
};

InferenceState := {
    Queued,
    Preprocessing,
    Executing,
    Postprocessing,
    Completed,
    Failed,
    TimedOut
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
InferenceEngine LOADS ModelVersion;
InferenceRequest TARGETS ModelOrEndpoint;
Preprocessor TRANSFORMS RawInput TO ModelInput;
ExecutionGraph CONSISTS_OF Operator;
Operator DISPATCHES_TO Kernel;
Kernel EXECUTES_ON Device;
InferenceEngine PRODUCES OutputTensor;
Postprocessor TRANSFORMS OutputTensor TO PredictionOrResponse;
ModelServer EXPOSES InferenceEngine;

# ---------- INFERENCE FLOW ----------
InferenceRequest
    -> ValidateRequest
    -> SelectModelVersion
    -> PreprocessInput
    -> ConstructInputTensor
    -> QueueOrBatch
    -> AllocateExecutionResources
    -> ExecuteModel
    -> ProduceOutputTensor
    -> PostprocessOutput
    -> ConstructResponse
    -> ReturnInferenceResult;

# ---------- MODEL ----------
InferenceModel :=
    TrainedModelArtifactPreparedForPrediction;

Model MAY_INCLUDE {
    Architecture,
    Parameters,
    InputSchema,
    OutputSchema,
    PreprocessingMetadata
};

ModelVersion IDENTIFIES SpecificModelState;

# ---------- MODEL LOADING ----------
ModelLoading :=
    ModelArtifact
    -> ValidateFormat
    -> LoadParameters
    -> BuildOrLoadExecutionGraph
    -> AllocatePersistentMemory
    -> Ready;

IF ModelLoadFails
THEN ModelAvailability := false;

# ---------- EXECUTION GRAPH ----------
ExecutionGraph :=
    DirectedGraph(
        Operators,
        TensorDependencies
    );

ExecutionEngine MUST_RESPECT DataDependencies;

# ---------- INPUT ----------
InferenceInput :=
    DataSubmittedForModelPredictionOrGeneration;

Input MAY_BE {
    NumericTensor,
    Text,
    Image,
    Audio,
    StructuredRecord,
    MultimodalInput
};

InputValidation CHECKS CompatibilityWithModelInputContract;

# ---------- PREPROCESSING ----------
Preprocessing :=
    RawInput
    -> Validate
    -> Transform
    -> Encode
    -> Normalise
    -> InputTensor;

Preprocessing MUST_BE SemanticallyCompatibleWithModelTrainingPipeline;

# ---------- TOKENISATION ----------
TextModel MAY_REQUIRE Tokenisation;

Tokenisation :=
    Text
    -> TokenSequence
    -> TokenIdentifiers;

TokenizerVersion SHOULD_BE CompatibleWith ModelVersion;

# ---------- TENSOR ----------
Tensor :=
    MultidimensionalArrayUsedByModelExecution;

Tensor HAS {
    Shape,
    DataType,
    DeviceLocation
};

InputShape MAY_BE {
    Fixed,
    Dynamic
};

# ---------- BATCH ----------
Batch :=
    SET_OR_SEQUENCE_OF(InferenceInputsProcessedTogether);

BatchSize :=
    NumberOfInputsInBatch;

LargerBatch MAY_INCREASE Throughput;
LargerBatch MAY_INCREASE QueueingOrExecutionLatency;

# ---------- STATIC BATCHING ----------
StaticBatching :=
    CallerOrPipelineFormsBatchBeforeInference;

# ---------- DYNAMIC BATCHING ----------
DynamicBatching :=
    RequestQueue
    -> CollectCompatibleRequests
    -> WaitWithinBatchWindow
    -> FormBatch
    -> Execute
    -> SplitOutputs;

DynamicBatching MAY_IMPROVE AcceleratorUtilisation;

# ---------- REQUEST QUEUE ----------
RequestQueue STORES PendingInferenceRequests;

QueueTime :=
    ExecutionStartTime - RequestArrivalTime;

IF ArrivalRate > SustainableServiceRate
THEN QueueDepth MAY_INCREASE;

# ---------- SCHEDULER ----------
InferenceScheduler :=
    PendingRequests
    + ModelAvailability
    + DeviceCapacity
    -> ExecutionAssignment;

Scheduler MAY_CONSIDER {
    Priority,
    BatchCompatibility,
    Deadline,
    ModelVersion,
    DeviceAvailability
};

# ---------- DEVICE ----------
InferenceDevice MAY_BE {
    CPU,
    GPU,
    AIAccelerator,
    EdgeDevice
};

DeviceSelection DEPENDS_ON {
    ModelCompatibility,
    LatencyTarget,
    ThroughputTarget,
    MemoryCapacity,
    Cost
};

# ---------- OPERATOR ----------
Operator :=
    ModelComputationUnit;

Operator MAY_INCLUDE {
    MatrixMultiplication,
    Convolution,
    Attention,
    Activation,
    Normalisation,
    Reduction
};

# ---------- KERNEL ----------
Kernel :=
    HardwareSpecificImplementationOfOperator;

KernelSelection :=
    Operator
    + Device
    + Shape
    + DataType
    -> SelectedKernel;

# ---------- EXECUTION ----------
ModelExecution :=
    PrepareExecutionContext
    -> ResolveReadyOperators
    -> DispatchKernels
    -> SynchroniseDependencies
    -> ProduceOutputs;

# ---------- ASYNCHRONOUS EXECUTION ----------
InferenceEngine MAY_EXECUTE OperationsAsynchronously;

AsyncExecution MAY_OVERLAP {
    DataTransfer,
    Compute,
    IndependentRequests
};

# ---------- MEMORY ----------
InferenceMemory MAY_INCLUDE {
    ModelParameterMemory,
    InputMemory,
    ActivationMemory,
    OutputMemory,
    WorkspaceMemory,
    CacheMemory
};

MemoryPlanner MAY_REUSE BuffersAcrossNonOverlappingTensorLifetimes;

# ---------- MODEL PARAMETER MEMORY ----------
ModelParameters MAY_BE {
    ResidentInDeviceMemory,
    ShardedAcrossDevices,
    Offloaded,
    LoadedOnDemand
};

# ---------- ACTIVATION MEMORY ----------
InferenceActivations :=
    IntermediateTensorsProducedDuringForwardExecution;

Inference MAY_REUSE ActivationBuffersAfterLastConsumerCompletes;

# ---------- KV CACHE ----------
AutoregressiveModel MAY_USE KeyValueCache;

KVCache :=
    StoredAttentionStateFromPreviousGenerationSteps;

KVCache MAY_REDUCE RepeatedComputation;
KVCache MAY_CONSUME SignificantMemory;

# ---------- AUTOREGRESSIVE INFERENCE ----------
AutoregressiveGeneration :=
    EncodePrompt
    -> ExecuteModel
    -> SelectNextToken
    -> AppendToken
    -> UpdateCache
    -> RepeatUntilStopCondition;

StopCondition MAY_INCLUDE {
    EndToken,
    MaximumLength,
    ExternalCancellation
};

# ---------- DECODING ----------
GenerativeInference MAY_USE DecodingStrategy;

DecodingStrategy MAY_INCLUDE {
    GreedySelection,
    BeamSearch,
    Sampling,
    TopKSampling,
    TopPSampling
};

DecodingParameters AFFECT OutputDistribution;

# ---------- STREAMING ----------
StreamingInference :=
    ProducePartialOutputBeforeFullInferenceCompletes;

Streaming MAY_REDUCE TimeToFirstOutput;

Stream MAY_DELIVER {
    Token,
    AudioChunk,
    PartialPrediction,
    Event
};

# ---------- POSTPROCESSING ----------
Postprocessing :=
    OutputTensor
    -> Decode
    -> Transform
    -> ApplyThresholdOrFormatting
    -> FinalResult;

Postprocessing MAY_INCLUDE {
    ClassSelection,
    ProbabilityNormalisation,
    TokenDecoding,
    BoundingBoxConversion
};

# ---------- PREDICTION ----------
Prediction :=
    ModelDerivedOutputForInput;

Prediction MAY_INCLUDE {
    Label,
    Score,
    Probability,
    NumericEstimate,
    GeneratedSequence,
    Embedding
};

# ---------- MODEL SERVER ----------
ModelServer :=
    ServingRuntimeHostingOneOrMoreInferenceEnginesOrModels;

ModelServer MAY_SUPPORT {
    VersionRouting,
    DynamicBatching,
    HealthChecks,
    Metrics,
    MultipleDevices
};

# ---------- ENDPOINT ----------
InferenceEndpoint :=
    NetworkOrApplicationInterfaceReceivingInferenceRequests;

Endpoint MAY_BE {
    HTTP,
    RPC,
    MessageQueue,
    LocalAPI
};

# ---------- REPLICA ----------
ServingReplica :=
    IndependentServingInstanceCapableOfInference;

MultipleReplicas MAY_INCREASE {
    Capacity,
    Availability
};

# ---------- LOAD BALANCING ----------
LoadBalancer DISTRIBUTES RequestsAcross HealthyReplicas;

Routing MAY_CONSIDER {
    Load,
    ModelVersion,
    DeviceType,
    Locality
};

# ---------- AUTOSCALING ----------
Autoscaling :=
    ObserveServingDemand
    -> ComparePolicy
    -> AddOrRemoveReplicas;

ScalingSignal MAY_INCLUDE {
    RequestRate,
    QueueDepth,
    Latency,
    DeviceUtilisation
};

# ---------- MODEL VERSION ROUTING ----------
VersionRouting MAY_SUPPORT {
    StableVersion,
    CanaryVersion,
    ABTestVersion,
    ShadowVersion
};

Request MAY_BE RoutedTo SelectedModelVersion;

# ---------- CACHING ----------
InferenceCache :=
    StoredResultForEquivalentEligibleInputOrIntermediateState;

Cache MAY_REDUCE RepeatedInferenceCost;

CacheUse MUST_RESPECT {
    InputIdentity,
    ModelVersion,
    Privacy,
    Freshness
};

# ---------- WARMUP ----------
ModelWarmup :=
    ExecuteRepresentativeOrSyntheticInputs
    TO InitialiseCompilationCachesAndDeviceState;

Warmup MAY_REDUCE InitialRequestLatency;

# ---------- COMPILATION ----------
InferenceEngine MAY_COMPILE ModelForTargetHardware;

Compilation MAY_INCLUDE {
    OperatorFusion,
    ConstantFolding,
    KernelSelection,
    LayoutOptimisation,
    MemoryPlanning
};

# ---------- QUANTISATION ----------
QuantisedInference :=
    ExecuteSelectedOperationsUsingReducedPrecision;

Quantisation MAY_REDUCE {
    ModelMemory,
    MemoryBandwidth,
    ComputeCost
};

Quantisation MAY_CHANGE PredictionQuality;

# ---------- PRUNING ----------
PrunedModel :=
    ModelWithSelectedParametersOrStructuresRemoved;

Pruning MAY_REDUCE ComputeOrMemory
IF RuntimeAndHardwareExploitResultingSparsityOrStructure;

# ---------- DISTILLATION ----------
DistilledModel :=
    SmallerModelTrainedToApproximateLargerTeacherModel;

Distillation MAY_IMPROVE ServingEfficiency;

# ---------- LATENCY ----------
EndToEndLatency :=
    QueueTime
    + PreprocessingTime
    + ModelExecutionTime
    + PostprocessingTime
    + TransportTime;

TimeToFirstOutput :=
    FirstOutputTime - RequestArrivalTime;

# ---------- THROUGHPUT ----------
InferenceThroughput :=
    CompletedInputs / Time;

# ---------- SERVICE LEVEL ----------
ServingObjective MAY_DEFINE {
    LatencyTarget,
    ThroughputTarget,
    AvailabilityTarget,
    ErrorRateTarget
};

# ---------- HEALTH CHECK ----------
HealthCheck MAY_VERIFY {
    ProcessHealth,
    ModelLoaded,
    DeviceAvailable,
    DependencyAvailable
};

IF ReplicaUnhealthy
THEN LoadBalancer SHOULD_STOP NormalRoutingToReplica;

# ---------- FAILURE ----------
InferenceFailure := {
    InvalidInput,
    ModelUnavailable,
    OutOfMemory,
    DeviceFailure,
    KernelFailure,
    Timeout,
    DependencyFailure
};

FailureHandling MAY_INCLUDE {
    Retry,
    Fallback,
    Reroute,
    ErrorResponse
};

# ---------- FALLBACK MODEL ----------
FallbackModel :=
    AlternativeModelUsedWhenPreferredModelUnavailableOrTooExpensive;

Fallback MAY_HAVE DifferentQualityOrLatencyCharacteristics;

# ---------- OBSERVABILITY ----------
InferenceObservability MAY_INCLUDE {
    RequestCount,
    Latency,
    QueueDepth,
    BatchSize,
    Throughput,
    ErrorRate,
    DeviceUtilisation,
    ModelVersion
};

Trace MAY_CONNECT RequestToPreprocessingExecutionAndPostprocessing;

# ---------- MODEL MONITORING ----------
ProductionMonitoring MAY_TRACK {
    InputDistribution,
    OutputDistribution,
    Confidence,
    Drift,
    QualityWhereGroundTruthAvailable
};

InferenceEngineMetrics SUPPORT OperationalMonitoring;
ModelMonitoring SUPPORT BehaviouralMonitoring;

# ---------- SECURITY ----------
InferenceSecurity MAY_INCLUDE {
    Authentication,
    Authorisation,
    InputValidation,
    ModelIntegrity,
    RateLimiting,
    Isolation,
    Encryption
};

UntrustedInput SHOULD_BE ValidatedBeforeModelExecution;

# ---------- PRIVACY ----------
InferenceRequest MAY_CONTAIN SensitiveData;

PrivacyControl MAY_INCLUDE {
    DataMinimisation,
    LoggingRestriction,
    Encryption,
    RetentionControl,
    CacheRestriction
};

# ---------- COST ----------
InferenceCost :=
    ComputeUse
    + MemoryUse
    + ServingInfrastructure
    + NetworkTransfer;

CostPerInference :=
    TotalServingCost / CompletedInferences;

# ---------- ENERGY ----------
InferenceEnergy :=
    PowerConsumption
    * ExecutionTime;

OptimisedInference MAY_REDUCE EnergyPerResult;

# ---------- SEMANTIC REASONING ----------
IF SoftwareLoadsTrainedModelAndExecutesItToProducePredictions
THEN AIInferenceEngineClassification := supported;

IF BatchSizeIncreasesWithinCapacity
THEN Throughput MAY_INCREASE;

IF QueueDepthContinuouslyIncreases
THEN ServingCapacity MAY_BE Insufficient;

IF ModelMemoryExceedsDeviceCapacity
THEN ShardingOffloadOrAlternativeModel MAY_BE_REQUIRED;

IF QuantisationApplied
THEN ModelQuality SHOULD_BE Revalidated;

IF ReplicaUnhealthy
AND HealthyReplicaExists
THEN Request SHOULD_BE Rerouted;

IF KVCacheUsedForAutoregressiveGeneration
THEN RepeatedAttentionComputation MAY_DECREASE
AND MemoryConsumption MAY_INCREASE;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "AI inference engine" ~= "artificial intelligence inference engine";
    "inference runtime" ~= "model inference runtime";
    "model serving engine" ~= "inference serving engine";
    "prediction engine" ~= "model inference engine" WHEN context == AI;
    "model server" ~= "AI model serving server";
    "dynamic batching" ~= "runtime request batching";
    "inference endpoint" ~= "model prediction endpoint";
    "KV cache" ~= "key-value attention cache" WHEN context == autoregressive_model;
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_MODEL_VERSION(q) ->
    IDENTIFY_INFERENCE_INPUT(q) ->
    IDENTIFY_PREPROCESSING(q) ->
    IDENTIFY_BATCHING_AND_QUEUE(q) ->
    IDENTIFY_DEVICE_AND_EXECUTION(q) ->
    IDENTIFY_OUTPUT_AND_POSTPROCESSING(q) ->
    IDENTIFY_SERVING_PERFORMANCE_SECURITY(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        inference_semantic_consistency,
        model_runtime_evidence,
        serving_evidence,
        performance_evidence,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: AIInferenceEngine;
    ontology: AIInferenceEngineOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_model_version:
        validate
        -> identify_input_output_contract
        -> identify_runtime_requirements
        -> identify_device_compatibility
        -> integrate;

    new_inference_request:
        validate_input
        -> identify_model
        -> identify_batching
        -> record_execution
        -> record_result
        -> integrate;

    new_serving_evidence:
        update_latency
        -> update_throughput
        -> update_queue_depth
        -> update_error_rate
        -> integrate;

    engine_revision:
        update_compilation
        -> update_batching
        -> update_memory_plan
        -> update_routing
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_inference_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + InferenceEntity
    + ExecutionEntity
    + ServingEntity
    + InferenceState
    + ModelExecution
    + DynamicBatching
    + InferenceObservability
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
AIInferenceEngineOntology
    -> ReceiveInferenceRequest
    -> ValidateAndPreprocessInput
    -> SelectModelVersion
    -> QueueAndBatchRequest
    -> AllocateDeviceResources
    -> ExecuteModelGraph
    -> ProduceOutputTensor
    -> PostprocessPrediction
    -> StreamOrReturnResult
    -> MeasureLatencyAndThroughput
    -> ScaleRouteAndRecoverServing
    -> MonitorModelAndInfrastructureBehaviour
    -> MaintainEfficientReliableInference;

END FLOW