Edge AI Computing Ontology

FLOW ALGEBRA
Edge AI Computing Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/emerging-computing/edge-ai-computing-ontology.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "EdgeAIComputingOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := EmergingComputing
ONTOLOGY := EdgeAIComputingOntology

# ---------- PRIMARY ENTITY ----------
ENTITY EdgeAIComputing {
    definition:
        "The execution of artificial intelligence and machine learning
         workloads on or near the devices and physical environments where
         data is generated, reducing dependence on remote centralised
         computing for every inference or decision.";

    objectives: [
        LocalInference,
        LowLatencyDecisionMaking,
        ReducedNetworkDependence,
        PrivacyPreservingProcessing,
        BandwidthEfficiency,
        EnergyAwareAI,
        ResilientIntelligence
    ];
}

# ---------- CORE ENTITIES ----------
EdgeAIEntity := {
    EdgeDevice,
    Sensor,
    AIModel,
    InferenceRuntime,
    Accelerator,
    EdgeGateway,
    CloudService,
    Application
};

DeviceEntity := {
    Microcontroller,
    MobileDevice,
    EmbeddedSystem,
    SingleBoardComputer,
    IndustrialController,
    SmartCamera,
    Robot
};

OperationalEntity := {
    InferenceRequest,
    LocalPrediction,
    ModelUpdate,
    Telemetry,
    NetworkConnection,
    Cache,
    DataBuffer
};

ConstraintEntity := {
    LatencyBudget,
    MemoryLimit,
    ComputeLimit,
    EnergyBudget,
    BandwidthLimit,
    ThermalLimit
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
Sensor PRODUCES Data;
EdgeDevice RECEIVES LocalData;
AIModel RUNS_ON InferenceRuntime;
InferenceRuntime EXECUTES_ON EdgeDevice;
Accelerator MAY_ACCELERATE AIModel;
EdgeGateway MAY_AGGREGATE MultipleEdgeDevices;
CloudService MAY_TRAIN_OR_UPDATE AIModel;
EdgeDevice MAY_OPERATE_WITH IntermittentConnectivity;

# ---------- EDGE AI FLOW ----------
PhysicalEnvironment
    -> SensorCapturesData
    -> EdgeDeviceReceivesInput
    -> PreprocessLocally
    -> ExecuteAIModel
    -> ProducePrediction
    -> TriggerLocalDecisionOrAction
    -> StoreOrTransmitSelectedResult
    -> MonitorDeviceAndModel
    -> ReceiveModelUpdateWhenAvailable;

# ---------- EDGE DEVICE ----------
EdgeDevice :=
    ComputingSystemLocatedNearDataSourceOrActuator;

EdgeDevice MAY_HAVE {
    CPU,
    GPU,
    NPU,
    DSP,
    Memory,
    Storage,
    NetworkInterface
};

# ---------- EDGE ----------
Edge :=
    ComputationalLocationNearDataGenerationConsumptionOrPhysicalAction;

Edge IS_RELATIVE_TO SystemArchitecture;

# ---------- AI MODEL ----------
EdgeAIModel :=
    MachineLearningModelPreparedForResourceConstrainedLocalExecution;

Model MAY_BE {
    NeuralNetwork,
    DecisionTree,
    StatisticalModel,
    VisionModel,
    AudioModel,
    LanguageModel
};

# ---------- LOCAL INFERENCE ----------
LocalInference :=
    InputData
    -> OnDevicePreprocessing
    -> ModelExecution
    -> Prediction;

LocalInference DOES_NOT_REQUIRE RemoteRoundTripForEachPrediction;

# ---------- LATENCY ----------
EndToEndEdgeLatency :=
    SensorAcquisitionTime
    + PreprocessingTime
    + InferenceTime
    + DecisionTime;

LocalExecution MAY_REDUCE NetworkRoundTripLatency;

# ---------- REAL-TIME ----------
RealTimeRequirement :=
    ResultMustBeProducedWithinDefinedDeadline;

IF InferenceLatency > Deadline
THEN RealTimeRequirement := violated;

# ---------- BANDWIDTH ----------
EdgeProcessing MAY_REDUCE DataSentToCloud;

BandwidthReduction :=
    RawDataVolume - TransmittedProcessedDataVolume;

# ---------- PRIVACY ----------
LocalProcessing MAY_KEEP SensitiveRawDataOnDevice;

PrivacyBenefit DEPENDS_ON {
    DataRetention,
    TelemetryPolicy,
    ModelBehaviour,
    Security
};

LocalProcessing DOES_NOT_AUTOMATICALLY Guarantee Privacy;

# ---------- CONNECTIVITY ----------
ConnectivityState MAY_BE {
    Online,
    Offline,
    Intermittent,
    LimitedBandwidth
};

EdgeAI SHOULD_DEFINE BehaviourForConnectivityLossWhereOperationallyRelevant;

# ---------- OFFLINE OPERATION ----------
OfflineInference :=
    ExecuteModelWithoutCurrentCloudConnection;

OfflineCapability MAY_SUPPORT {
    Resilience,
    RemoteOperation,
    ReducedNetworkDependence
};

# ---------- EDGE GATEWAY ----------
EdgeGateway :=
    IntermediateComputeNodeBetweenDevicesAndCloudOrCoreNetwork;

Gateway MAY {
    AggregateData,
    ExecuteModels,
    FilterEvents,
    CoordinateUpdates,
    TranslateProtocols
};

# ---------- CLOUD RELATIONSHIP ----------
Cloud MAY_SUPPORT {
    ModelTraining,
    FleetManagement,
    LongTermStorage,
    GlobalAnalytics,
    ModelDistribution
};

EdgeAndCloud MAY_FORM HybridAIArchitecture;

# ---------- HYBRID INFERENCE ----------
HybridInference :=
    LocalModelExecution
    + OptionalRemoteModelOrService;

RoutingDecision MAY_DEPEND_ON {
    Confidence,
    Connectivity,
    Latency,
    Privacy,
    ModelCapability
};

# ---------- CASCADE ----------
EdgeModel MAY_HANDLE EasyOrTimeCriticalCases;

IF EdgeConfidenceLow
AND NetworkAvailable
THEN Request MAY_BE EscalatedToCloudModel;

# ---------- SENSOR ----------
Sensor MAY_CAPTURE {
    Image,
    Audio,
    Motion,
    Temperature,
    Pressure,
    Position,
    IndustrialSignal
};

# ---------- ACTUATOR ----------
EdgeAIResult MAY_CONTROL Actuator;

Actuator MAY_INCLUDE {
    Motor,
    Relay,
    Valve,
    Display,
    Alarm
};

SafetyCriticalActuation SHOULD_HAVE AppropriateValidationAndFailSafeControls;

# ---------- PREPROCESSING ----------
EdgePreprocessing MAY_INCLUDE {
    Resize,
    Normalise,
    Filter,
    FeatureExtraction,
    Tokenisation,
    SignalTransform
};

Preprocessing MUST_MATCH ModelInputContract;

# ---------- POSTPROCESSING ----------
EdgePostprocessing MAY_INCLUDE {
    Threshold,
    Decode,
    NonMaximumSuppression,
    ClassificationDecision,
    ControlMapping
};

# ---------- AI ACCELERATOR ----------
EdgeAccelerator MAY_BE {
    NPU,
    GPU,
    DSP,
    FPGA,
    DedicatedASIC
};

Accelerator MAY_IMPROVE {
    InferenceLatency,
    Throughput,
    EnergyEfficiency
};

# ---------- MICROCONTROLLER AI ----------
TinyML :=
    MachineLearningExecutionOnHighlyResourceConstrainedMicrocontrollersOrEmbeddedSystems;

TinyML MAY_HAVE {
    KilobyteOrMegabyteScaleMemoryConstraints,
    LowPowerBudget,
    LimitedCompute
};

# ---------- MODEL SIZE ----------
ModelMemoryFootprint :=
    ParameterMemory
    + RuntimeMetadata
    + ActivationMemory;

Model MUST_FIT AvailableDeviceResourcesOrUseStreamingPartitioningOrAlternativeArchitecture;

# ---------- QUANTISATION ----------
ModelQuantisation :=
    ConvertSelectedModelValuesOrOperationsToReducedPrecision;

Quantisation MAY_REDUCE {
    ModelSize,
    MemoryBandwidth,
    ComputeCost,
    Energy
};

Quantisation MAY_AFFECT Accuracy;

# ---------- PRUNING ----------
ModelPruning :=
    RemoveSelectedWeightsChannelsOrStructures;

Pruning MAY_REDUCE Compute
IF RuntimeAndHardwareExploitResultingSparsityOrSmallerStructure;

# ---------- DISTILLATION ----------
KnowledgeDistillation :=
    TrainSmallerStudentModelUsingTeacherModelInformation;

Distillation MAY_CREATE EdgeSuitableModel;

# ---------- MODEL COMPRESSION ----------
EdgeModelCompression MAY_INCLUDE {
    Quantisation,
    Pruning,
    Distillation,
    WeightSharing,
    ArchitectureOptimisation
};

# ---------- COMPILATION ----------
EdgeAICompiler :=
    Model
    -> OptimiseGraph
    -> SelectKernels
    -> AllocateMemory
    -> GenerateDeviceExecutable;

Compiler SHOULD_TARGET SpecificHardwareCapabilities;

# ---------- OPERATOR SUPPORT ----------
EdgeRuntime MAY_NOT_SUPPORT EveryModelOperator;

UnsupportedOperator MAY_REQUIRE {
    GraphRewrite,
    Fallback,
    CustomKernel,
    ModelRedesign
};

# ---------- MEMORY ----------
EdgeMemory MAY_INCLUDE {
    Flash,
    RAM,
    AcceleratorMemory,
    PersistentStorage
};

PeakActivationMemory MAY_LIMIT DeployableModelSize;

# ---------- ENERGY ----------
EnergyPerInference :=
    EnergyConsumed / CompletedInference;

EdgeEnergyBudget MAY_BE ConstrainedBy {
    Battery,
    ThermalDesign,
    PowerSupply
};

# ---------- DUTY CYCLING ----------
DutyCycling :=
    AlternateActiveAndLowPowerStates;

EventTriggeredInference MAY_REDUCE EnergyComparedWithContinuousFullProcessing;

# ---------- THERMAL ----------
SustainedInference MAY_INCREASE DeviceTemperature;

ThermalThrottling MAY_REDUCE ComputePerformance;

# ---------- PERFORMANCE ----------
EdgeAIPerformance MAY_BE_MEASURED_BY {
    Latency,
    Throughput,
    Accuracy,
    EnergyPerInference,
    MemoryUse,
    ModelSize
};

# ---------- ACCURACY ----------
CompressionAndReducedPrecision SHOULD_BE EvaluatedAgainst RequiredModelQuality;

# ---------- ON-DEVICE LEARNING ----------
EdgeDevice MAY_SUPPORT {
    FineTuning,
    OnlineLearning,
    Personalisation,
    ContinualLearning
};

OnDeviceTraining REQUIRES MoreComputeMemoryAndEnergyThanInferenceInManyCases;

# ---------- FEDERATED LEARNING ----------
FederatedLearning :=
    MultipleEdgeClientsTrainLocally
    -> SendModelUpdatesOrDerivedInformation
    -> Aggregate
    -> DistributeUpdatedGlobalModel;

FederatedLearning MAY_REDUCE NeedToCentraliseRawTrainingData;

FederatedLearning DOES_NOT_BY_ITSELF Guarantee PrivacyOrSecurity;

# ---------- MODEL UPDATE ----------
ModelUpdate :=
    NewModelVersion
    -> Download
    -> VerifyIntegrity
    -> ValidateCompatibility
    -> Install
    -> Activate;

# ---------- OVER-THE-AIR UPDATE ----------
OTAUpdate :=
    RemoteDistributionOfSoftwareOrModelUpdate;

Update SHOULD_SUPPORT {
    Authentication,
    IntegrityVerification,
    Rollback
};

# ---------- ROLLBACK ----------
IF NewModelFailsHealthOrQualityChecks
THEN Device MAY_RESTORE PreviousKnownGoodModel;

# ---------- FLEET MANAGEMENT ----------
EdgeFleet :=
    SET_OF(ManagedEdgeDevices);

FleetManagement MAY_TRACK {
    DeviceVersion,
    ModelVersion,
    Health,
    Connectivity,
    HardwareType
};

# ---------- DEVICE HETEROGENEITY ----------
EdgeFleet MAY_CONTAIN DifferentHardwareCapabilities;

DeploymentPolicy SHOULD_MATCH ModelVariantToDeviceCapability;

# ---------- MODEL VARIANT ----------
ModelFamily MAY_HAVE {
    SmallVariant,
    MediumVariant,
    HighAccuracyVariant
};

Device MAY_RECEIVE VariantBasedOn ResourceProfile;

# ---------- TELEMETRY ----------
EdgeTelemetry MAY_INCLUDE {
    InferenceLatency,
    ErrorRate,
    ModelVersion,
    DeviceTemperature,
    MemoryUse,
    PredictionStatistics
};

Telemetry SHOULD_RESPECT PrivacyAndBandwidthConstraints;

# ---------- OBSERVABILITY ----------
EdgeObservability :=
    LocalMetrics
    + Logs
    + HealthEvents
    + OptionalRemoteTelemetry;

# ---------- DRIFT ----------
EdgeInputDistribution MAY_CHANGEOverTime;

DataDrift MAY_REDUCE ModelQuality;

DriftDetection MAY_RUN {
    OnDevice,
    Gateway,
    Cloud
};

# ---------- MODEL MONITORING ----------
GroundTruth MAY_BE DelayedOrUnavailableAtEdge;

ModelQualityMonitoring MAY_REQUIRE SampledFeedbackOrLaterLabels;

# ---------- SECURITY ----------
EdgeAISecurity MAY_INCLUDE {
    SecureBoot,
    ModelIntegrity,
    DeviceIdentity,
    Encryption,
    AccessControl,
    UpdateAuthentication,
    RuntimeIsolation
};

# ---------- PHYSICAL ACCESS ----------
EdgeDevice MAY_BE PhysicallyAccessibleToAdversary;

ThreatModel SHOULD_CONSIDER {
    DeviceTampering,
    ModelExtraction,
    SecretExtraction,
    SensorSpoofing
};

# ---------- ADVERSARIAL INPUT ----------
SensorInput MAY_BE ManipulatedToInfluenceModelPrediction;

RobustnessTesting SHOULD_INCLUDE RelevantPhysicalAndDigitalPerturbations;

# ---------- MODEL CONFIDENTIALITY ----------
ModelArtifact MAY_BE ValuableIntellectualProperty;

Protection MAY_INCLUDE {
    EncryptionAtRest,
    SecureExecution,
    AccessControl
};

# ---------- FAIL SAFE ----------
SafetyCriticalEdgeAI SHOULD_DEFINE SafeBehaviourFor {
    ModelFailure,
    SensorFailure,
    PowerFailure,
    ConnectivityFailure
};

# ---------- REDUNDANCY ----------
CriticalSystem MAY_USE MultipleSensorsModelsOrControllers;

Redundancy MAY_IMPROVE FaultTolerance;

# ---------- EDGE CACHE ----------
EdgeDevice MAY_CACHE {
    Model,
    Configuration,
    ReferenceData,
    RecentResults
};

Caching MAY_SUPPORT OfflineOperationAndLowerLatency;

# ---------- DATA BUFFER ----------
If NetworkUnavailable:
    SelectedTelemetry MAY_BE BufferedLocally
    -> TransmittedLaterAccordingToPolicy;

BufferCapacity MUST_BE Managed;

# ---------- APPLICATIONS ----------
EdgeAI MAY_SUPPORT {
    SmartCamera,
    Robotics,
    IndustrialMonitoring,
    AutonomousSystems,
    Wearables,
    SmartHome,
    Agriculture,
    AudioProcessing
};

# ---------- SMART CAMERA ----------
SmartCamera :=
    Camera
    + EdgeCompute
    + VisionModel;

SmartCamera MAY_SEND MetadataInsteadOfRawVideo;

# ---------- INDUSTRIAL EDGE AI ----------
IndustrialEdgeAI MAY_PERFORM {
    AnomalyDetection,
    PredictiveMaintenance,
    QualityInspection,
    ProcessMonitoring
};

# ---------- ROBOTICS ----------
Robot MAY_REQUIRE LocalInferenceForLowLatencyControl;

CloudOnlyControl MAY_BE UnsuitableForTimeCriticalLoops;

# ---------- BENCHMARKING ----------
EdgeAIBenchmark SHOULD_REPORT {
    Hardware,
    Model,
    Accuracy,
    Latency,
    Energy,
    Memory,
    InputSize
};

CloudAndEdgeComparison SHOULD_INCLUDE NetworkCostAndLatencyWhereRelevant;

# ---------- COST ----------
EdgeAICost MAY_INCLUDE {
    DeviceHardware,
    Energy,
    FleetManagement,
    Connectivity,
    CloudSupport
};

# ---------- SUSTAINABILITY ----------
EdgeProcessing MAY_REDUCE NetworkTransfer
BUT LargeDeviceFleets MAY_INCREASE HardwareAndLifecycleImpact;

# ---------- SEMANTIC REASONING ----------
IF AIModelExecutesNearDataSource
THEN EdgeAIComputingClassification := supported;

IF NetworkUnavailable
AND ModelAndRequiredDataAreLocal
THEN LocalInference MAY_CONTINUE;

IF ModelExceedsDeviceMemory
THEN CompressionPartitioningOrDifferentModel MAY_BE_REQUIRED;

IF QuantisationReducesPrecision
THEN Accuracy SHOULD_BE Revalidated;

IF RawDataProcessedLocallyAndNotTransmitted
THEN NetworkBandwidthUse MAY_DECREASE;

IF DeviceThermallyThrottles
THEN InferenceLatency MAY_INCREASE;

IF ModelUpdateIntegrityCannotBeVerified
THEN Update SHOULD_NOT_BE Activated;

IF ClaimedEdgeAdvantageIgnoresCloudNetworkRoundTripOrDeviceEnergy
THEN ComparisonConfidence := reduced;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "edge AI" ~= "edge artificial intelligence";
    "edge AI computing" ~= "AI computing at the edge";
    "on-device AI" ~= "on-device machine learning";
    "local inference" ~= "on-device inference" WHEN execution_is_on_local_device;
    "edge accelerator" ~= "edge AI accelerator";
    "TinyML" ~= "tiny machine learning";
    "edge gateway" ~= "AI edge gateway" WHEN context == edge_AI;
    "federated learning" ~= "federated machine learning";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_EDGE_DEVICE(q) ->
    IDENTIFY_SENSOR_OR_INPUT(q) ->
    IDENTIFY_MODEL_AND_RUNTIME(q) ->
    IDENTIFY_DEVICE_RESOURCES(q) ->
    IDENTIFY_LOCAL_OR_HYBRID_INFERENCE(q) ->
    IDENTIFY_CONNECTIVITY_AND_UPDATES(q) ->
    IDENTIFY_SECURITY_ENERGY_PERFORMANCE(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        edge_architecture_consistency,
        device_evidence,
        model_runtime_evidence,
        operational_evidence,
        contextual_relevance
    );

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

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: EdgeAIComputing;
    ontology: EdgeAIComputingOntology;
    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_edge_device:
        validate
        -> identify_compute
        -> identify_memory
        -> identify_accelerator
        -> identify_connectivity
        -> integrate;

    new_model:
        validate_input_output
        -> identify_model_size
        -> identify_operator_support
        -> benchmark_on_target_device
        -> integrate;

    new_deployment:
        identify_device_group
        -> identify_model_version
        -> verify_compatibility
        -> deploy
        -> monitor
        -> integrate;

    ontology_revision:
        update_device_inventory
        -> update_model_variants
        -> update_runtime_metrics
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + EdgeAIEntity
    + DeviceEntity
    + OperationalEntity
    + ConstraintEntity
    + LocalInference
    + ModelCompression
    + FederatedLearning
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
EdgeAIComputingOntology
    -> CaptureDataNearSource
    -> PreprocessLocally
    -> SelectEdgeModelAndRuntime
    -> AllocateConstrainedDeviceResources
    -> ExecuteLocalInference
    -> ProduceImmediateDecisionOrAction
    -> TransmitOnlyRequiredDataOrMetadata
    -> ContinueUnderDefinedConnectivityLoss
    -> MonitorEnergyLatencyAndModelBehaviour
    -> SecurelyUpdateModelAcrossDeviceFleet
    -> DetectDriftFailureOrResourcePressure
    -> CoordinateEdgeGatewayAndCloudWhereNeeded
    -> MaintainResilientEfficientEdgeIntelligence;

END FLOW