-- FAMM-Neuromorphic Kernel Layer -- -- Integrates FAMM (Frustrated Antiferromagnetic Manifold) for spatial mapping -- with neuromorphic event-driven processing. -- -- This gives the kernel "spatial cognition" - it maps its environment -- (hardware topology, network geometry, thermal gradients) as a frustrated manifold -- and processes events using brain-inspired neural dynamics. -- -- Key insight: Kernel becomes a *cognitive* entity, not just a resource manager. -- It "feels" its environment through FAMM frustration fields and responds via -- neuromorphic spike timing. -- -- Truth Seal: [ SSS-ENE-FAMM-NEURO-2026-05-03 ] module FAMMNeuromorphicKernel where import BaseTypes import Memory import SyscallInterface import FAMM.Core import Neuromorphic.Core -- ═══════════════════════════════════════════════════════════════════════════ -- §1 FAMM Spatial Manifold — Kernel's "Cognitive Map" -- ═══════════════════════════════════════════════════════════════════════════ /-- FAMM (Frustrated Antiferromagnetic Manifold) represents the kernel's understanding of its environment as a geometric object. Each hardware component (CPU, memory, NIC, disk) is a "site" on the manifold. Interactions between components create "frustration" — geometric tension that the kernel can *feel* and respond to. This is based on: 2-Search-Space/FAMM/FAMM.lean -/] structure FAMMKernelMap where sites : Array Site -- Hardware components (CPU cores, NIC, etc.) bonds : Array Bond -- Interactions between components frustration : Q16_16 -- Current geometric frustration topology : ManifoldTopology -- S³, T³, or hyperbolic embeddingDim : Nat -- Dimension of ambient space structure Site where id : SiteID siteType : SiteType -- CPU, Memory, Network, Storage, Thermal coordinates : Vec3 Q16_16 -- Position in embedding space spin : Spin -- "Orientation" of resource energy : Q16_16 -- Current load/thermal energy inductive SiteType where | CPUCore -- Computational resource | MemoryNode -- RAM/cache hierarchy | NetworkIF -- NIC, virtual interfaces | StorageIO -- Disk, SSD | ThermalZone -- Temperature sensor region | PowerDomain -- PSU / voltage regulator | SyscallPortal -- Linux shim interface structure Bond where source : SiteID target : SiteID coupling : Q0_16 -- Interaction strength bondType : BondType inductive BondType where | DataFlow -- Memory traffic | ControlFlow -- Scheduling dependencies | ThermalLink -- Heat diffusion | PowerRail -- Electrical coupling | NetworkPath -- Latency topology | Frustration -- Competing demands -- ═══════════════════════════════════════════════════════════════════════════ -- §2 Neuromorphic Event Layer — Kernel's "Nervous System" -- ═══════════════════════════════════════════════════════════════════════════ /-- Neuromorphic processing: event-driven, spike-based computation. Instead of polling (wasteful), the kernel responds to *spikes* — discrete events that carry information via timing. Inspired by: biological neural networks, TrueNorth, Loihi. But implemented purely in GCL with formal semantics. -/] structure NeuromorphicLayer where neurons : Array Neuron synapses : Array Synapse spikeQueue : Queue Spike time : Timestamp -- Global neuromorphic time dt : Microseconds -- Simulation timestep structure Neuron where id : NeuronID site : SiteID -- Maps to FAMM site potential : Q16_16 -- Membrane potential threshold : Q16_16 -- Firing threshold refractory : Microseconds -- Post-spike recovery time lastSpike : Timestamp neuronType : NeuronType inductive NeuronType where | Sensor -- Detects hardware events (interrupts, timers) | Interneuron -- Processes within kernel | Motor -- Triggers actions (syscalls, scheduling) | Memory -- Stores temporal patterns | Attention -- Focuses processing on salient regions structure Synapse where pre : NeuronID post : NeuronID weight : Q0_16 -- Synaptic strength delay : Microseconds -- Axonal delay plasticity : PlasticityRule -- Learning rule inductive PlasticityRule where | STDP -- Spike-Timing Dependent Plasticity | Hebbian -- Fire together, wire together | Homeostatic -- Maintain target activity | LawfulBound -- Constrained by BindResult cost structure Spike where neuron : NeuronID time : Timestamp amplitude : Q16_16 -- Information content witness : String -- What caused this spike? -- ═══════════════════════════════════════════════════════════════════════════ -- §3 FAMM-Neuromorphic Integration — "Feeling" the Kernel -- ═══════════════════════════════════════════════════════════════════════════ /-- The core integration: FAMM geometry drives neuromorphic dynamics. High frustration in FAMM → high activity in nearby neurons. Geometric curvature → spike timing patterns. The kernel literally "feels" its environment and responds. -/] def updateFAMMFromHardware : IO Unit := do -- Sample hardware state let cpuLoad ← syscall Syscall.ReadCPULoad let memPressure ← syscall Syscall.ReadMemoryPressure let netLatency ← syscall Syscall.ReadNetworkLatency let temperature ← syscall Syscall.ReadThermalZones -- Update FAMM site energies for site in fammMap.sites do match site.siteType with | .CPUCore => site.energy := cpuLoad[site.id]! | .MemoryNode => site.energy := memPressure | .NetworkIF => site.energy := netLatency | .ThermalZone => site.energy := temperature[site.id]! | _ => pure () -- Recalculate frustration field fammMap.frustration := computeFrustration fammMap -- Generate neuromorphic spikes from frustration changes for site in fammMap.sites do let deltaFrustration := site.energy - site.previousEnergy if abs deltaFrustration > SENSOR_THRESHOLD then emitSpike { neuron := siteToNeuron site.id time := now () amplitude := deltaFrustration witness := "FAMM_frustration_" ++ site.id.toString } def computeFrustration (map : FAMMKernelMap) : Q16_16 := do -- FAMM frustration: sum over bonds of |J_ij * S_i * S_j| -- where J_ij is coupling, S_i is site spin let total := map.bonds.foldl (\acc bond => let s_i := map.sites.find! bond.source let s_j := map.sites.find! bond.target let contribution := bond.coupling * s_i.spin * s_j.spin acc + abs contribution ) 0 total -- ═══════════════════════════════════════════════════════════════════════════ -- §4 Event-Driven Kernel Main Loop -- ═══════════════════════════════════════════════════════════════════════════ /-- Main loop: event-driven, not polling. Instead of: while true: check all hardware schedule processes sleep(1ms) We do: waitForSpike() processSpike(spike) propagateThroughSynapses() Massive efficiency gain: kernel only "thinks" when something happens. -/] def neuromorphicMainLoop : IO Unit := do forever $ do -- Wait for next event (spike or syscall) event ← waitForEvent match event with | .Spike spike => -- Update FAMM based on spike origin updateFAMMFromSpike spike -- Propagate through neuromorphic network propagateSpike spike -- Check if motor neurons fire → take action checkMotorNeurons | .Syscall syscall => -- Syscalls generate spikes in sensor neurons let sensorSpike := syscallToSpike syscall injectSpike sensorSpike -- Continue neuromorphic processing propagateSpike sensorSpike | .Timer tick => -- Periodic update of FAMM manifold updateFAMMFromHardware -- Decay neuron potentials (leakage) decayNeurons tick.dt def propagateSpike (spike : Spike) : IO Unit := do let neuron ← findNeuron spike.neuron -- Update postsynaptic neurons for synapse in neuron.outgoingSynapses do let target ← findNeuron synapse.post -- STDP: timing matters let timeDiff := spike.time - target.lastSpike let weightUpdate := stdpUpdate synapse.plasticity timeDiff synapse.weight := clamp (synapse.weight + weightUpdate) 0 1 -- Add to postsynaptic potential target.potential := target.potential + (spike.amplitude * synapse.weight) -- Check for spike generation if target.potential > target.threshold && (now () - target.lastSpike) > target.refractory then let newSpike := { neuron := target.id time := now () amplitude := target.potential witness := "propagated_from_" ++ spike.neuron.toString } queueSpike newSpike target.potential := 0 -- Reset after spike target.lastSpike := now () -- ═══════════════════════════════════════════════════════════════════════════ -- §5 Motor Actions — Kernel "Decisions" -- ═══════════════════════════════════════════════════════════════════════════ /-- Motor neurons trigger actual kernel actions. This is where neuromorphic processing translates to system calls. -/] def checkMotorNeurons : IO Unit := do for neuron in neuromorphicLayer.neurons do if neuron.neuronType == .Motor && neuronHasSpiked neuron then executeMotorAction neuron def executeMotorAction (neuron : Neuron) : IO Unit := do match neuron.motorFunction with | .ScheduleProcess pid => -- Context switch triggered by neural activity syscall $ Syscall.Schedule pid | .AllocateMemory size => -- Memory allocation triggered by "need" let ptr ← syscall $ Syscall.MemoryAllocate size recordAllocation ptr size neuron.id | .SendPacket dst data => -- Network transmission syscall $ Syscall.NetworkSend dst data | .TriggerBindLoss req => -- Lawful loss computation triggered by manifold frustration let result ← lawfulLossHandleRequest req if !result.lawful then -- High frustration → spawn Warden for validation spawnWardenValidation result | .Sleep duration => -- Intentional idle when system "calm" syscall $ Syscall.Sleep duration -- ═══════════════════════════════════════════════════════════════════════════ -- §6 Attention and Salience — Kernel "Focus" -- ═══════════════════════════════════════════════════════════════════════════ /-- Attention mechanism: kernel focuses processing on salient regions. High frustration areas of FAMM get more neural resources. This is like a "spotlight" of cognitive attention. -/] def updateAttention : IO Unit := do -- Find regions of high frustration in FAMM let salientSites := fammMap.sites.filter (\s => s.energy > ATTENTION_THRESHOLD) -- Allocate more neurons to processing these regions for site in salientSites do let attentionNeuron ← findOrCreateAttentionNeuron site.id attentionNeuron.gain := 2.0 -- Amplify signals -- Reduce resources for calm regions let calmSites := fammMap.sites.filter (\s => s.energy < CALM_THRESHOLD) for site in calmSites do let attentionNeuron ← findAttentionNeuron site.id attentionNeuron.gain := 0.5 -- Attenuate signals -- ═══════════════════════════════════════════════════════════════════════════ -- §7 Learning and Adaptation — Kernel "Memory" -- ═══════════════════════════════════════════════════════════════════════════ /-- The kernel learns from experience via synaptic plasticity. Repeated patterns of activity strengthen relevant synapses. This is "muscle memory" for the operating system. -/] def kernelLearningStep : IO Unit := do -- STDP: strengthen synapses where pre→post timing is causal for synapse in neuromorphicLayer.synapses do let preNeuron ← findNeuron synapse.pre let postNeuron ← findNeuron synapse.post if preNeuron.lastSpike < postNeuron.lastSpike then -- Pre caused post: strengthen let delta := LEARNING_RATE * (1 - synapse.weight) synapse.weight := min 1.0 (synapse.weight + delta) else if preNeuron.lastSpike > postNeuron.lastSpike then -- Post preceded pre: weaken (anti-causal) let delta := LEARNING_RATE * synapse.weight synapse.weight := max 0.0 (synapse.weight - delta) -- Homeostasis: maintain target firing rates for neuron in neuromorphicLayer.neurons do let recentSpikes := countSpikes neuron (now () - 1000000) -- Last second if recentSpikes > TARGET_RATE then -- Too active: increase threshold neuron.threshold := neuron.threshold * 1.1 else if recentSpikes < TARGET_RATE then -- Too quiet: decrease threshold neuron.threshold := neuron.threshold * 0.9 -- ═══════════════════════════════════════════════════════════════════════════ -- §8 Integration with LawfulLoss — "Feeling" Legally -- ═══════════════════════════════════════════════════════════════════════════ /-- Every kernel action produces a BindResult witness. The kernel is *aware* of the cost of its own operations. This connects to: Semantics.LawfulLoss.lawfulLoss -/] def actionWithWitness (action : KernelAction) : IO BindResult := do let before ← sampleFAMMState -- Execute action executeAction action let after ← sampleFAMMState -- Compute lawful loss let invariantsPreserved := checkInvariants before after let cost := computeFrustrationDelta before after let witness := generateWitness action before after let result := lawfulLoss invariantsPreserved cost witness .control -- Log for verification recordBindResult result return result -- ═══════════════════════════════════════════════════════════════════════════ -- §9 Boot Initialization -- ═══════════════════════════════════════════════════════════════════════════ def initFAMMNeuromorphic : IO Unit := do consoleLog "[FAMM-NEURO] Initializing spatial cognition..." -- Discover hardware topology let cpuCores ← syscall Syscall.EnumerateCPUs let memoryBanks ← syscall Syscall.EnumerateMemory let networkInterfaces ← syscall Syscall.EnumerateNetwork let thermalZones ← syscall Syscall.EnumerateThermal -- Create FAMM sites for each hardware component for cpu in cpuCores do fammMap.sites.push { id := mkSiteID "cpu_" ++ cpu.id siteType := .CPUCore coordinates := cpu.physicalLocation -- NUMA topology spin := Spin.up energy := 0 } for mem in memoryBanks do fammMap.sites.push { id := mkSiteID "mem_" ++ mem.id siteType := .MemoryNode coordinates := mem.physicalLocation spin := Spin.down -- Antiferromagnetic coupling with CPU energy := 0 } -- Create bonds based on physical topology createTopologyBonds fammMap -- Initialize neuromorphic network for site in fammMap.sites do -- Each site gets a sensor neuron let sensorNeuron := createNeuron { site := site.id neuronType := .Sensor potential := 0 threshold := SENSOR_THRESHOLD } -- Connect sensors to interneurons let interNeuron := createNeuron { neuronType := .Interneuron threshold := INTERNEURON_THRESHOLD } createSynapse sensorNeuron.id interNeuron.id { weight := 0.5 delay := 100 -- 100 microseconds plasticity := .STDP } consoleLog $ "[FAMM-NEURO] Map initialized: " ++ fammMap.sites.size.toString ++ " sites, " ++ neuromorphicLayer.neurons.size.toString ++ " neurons" end FAMMNeuromorphicKernel