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485 lines
23 KiB
Text
485 lines
23 KiB
Text
/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
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Released under Apache 2.0 license as described in the file LICENSE.
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Authors: Research Stack Team
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StreamCompression.lean — DSP-Aware Stream Compression with Spectral Analysis
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This module formalizes DSP-aware compression for streaming data:
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- Sample-block processing (DSP standard)
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- Spectral redundancy detection (FFT-based)
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- Q16_16 fixed-point for hardware extraction
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- FPGA DSP slice integration (TSM opcodes 0x14, 0x42)
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- Energy-aware decompression scheduling
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Key insight:
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DSP compression treats data as continuous signals, not discrete bytes.
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Spectral analysis identifies redundancy in frequency domain, not just spatial.
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Per AGENTS.md §1.4: Q16_16 fixed-point for hardware extraction.
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Per AGENTS.md §2: PascalCase types, camelCase functions.
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Per AGENTS.md §4: Every def has eval witness or theorem.
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TODO(lean-port): Connect to FPGA Warden Node AMMR accumulator
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TODO(lean-port): Integrate PhiRedundancy 3-stream scheme as erasure coding
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TODO(lean-port): Integrate swarm design review
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-/
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import Mathlib.Data.Nat.Basic
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import Mathlib.Data.Real.Basic
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import Mathlib.Tactic
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import Semantics.FixedPoint
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import Semantics.SwarmDesignReview
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import Semantics.Timing
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namespace Semantics.StreamCompression
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open Semantics.Q16_16
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open Semantics.SwarmDesignReview
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open Semantics.Timing
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §0 DSP Sample Types
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- DSP sample in Q16.16 fixed-point format. -/
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abbrev DspSample := Q16_16
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/-- Sample block (DSP standard processing unit). -/
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structure SampleBlock where
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samples : Array DspSample
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sampleRate : Nat -- Hz
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deriving Repr, Inhabited
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/-- Frequency domain representation (FFT output). -/
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structure FrequencyDomain where
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real : Array DspSample -- Real components
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imag : Array DspSample -- Imaginary components
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deriving Repr, Inhabited
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/-- Spectral band for compression decisions. -/
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structure SpectralBand where
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lowFreq : Nat -- Lower bound (Hz)
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highFreq : Nat -- Upper bound (Hz)
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energy : Q16_16 -- Band energy (Q16.16)
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redundancy : Q16_16 -- Redundancy factor (0 = unique, 1 = fully redundant)
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deriving Repr, Inhabited
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §1 DSP Primitives (Fixed-Point)
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Compute energy of sample block: E = Σ x². -/
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def computeEnergy (block : SampleBlock) : Q16_16 :=
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block.samples.foldl (fun acc s => acc + s * s) zero
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/-- Compute mean of sample block. -/
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def computeMean (block : SampleBlock) : Q16_16 :=
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if block.samples.isEmpty then zero
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else
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let sum := block.samples.foldl (fun acc s => acc + s) zero
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div sum (ofNat block.samples.size)
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/-- Compute variance: Var = E[x²] - E[x]². -/
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def computeVariance (block : SampleBlock) : Q16_16 :=
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let mean := computeMean block
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let meanSq := mean * mean
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let energy := computeEnergy block
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let energyPerSample := div energy (ofNat block.samples.size)
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sub energyPerSample meanSq
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/-- Simple FIR filter: y[n] = Σ h[k] * x[n-k]. Stub: returns block unchanged. -/
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def firFilter (coeffs : Array Q16_16) (block : SampleBlock) : SampleBlock :=
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block
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §2 Spectral Analysis (FFT Approximation)
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Power-of-2 check for FFT. -/
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def isPowerOfTwo (n : Nat) : Bool :=
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n > 0 && (n &&& (n - 1)) = 0
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/-- Next power of 2 ≥ n (iterative, no recursion). -/
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def nextPowerOfTwo (n : Nat) : Nat :=
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if n = 0 then 1 else
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let n1 := n - 1
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let n2 := n1 ||| (n1 >>> 1)
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let n3 := n2 ||| (n2 >>> 2)
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let n4 := n3 ||| (n3 >>> 4)
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let n5 := n4 ||| (n4 >>> 8)
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let n6 := n5 ||| (n5 >>> 16)
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n6 + 1
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/-- Cooley-Tukey FFT (simplified, fixed-point).
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Note: Full FFT implementation requires complex arithmetic.
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This is a placeholder for spectral energy estimation. -/
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def computeFFT (block : SampleBlock) : FrequencyDomain :=
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let n := block.samples.size
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if ¬isPowerOfTwo n then
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-- Zero-pad to next power of 2
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let paddedSize := nextPowerOfTwo n
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let padded := Array.mk (List.replicate paddedSize zero)
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let filled := padded.foldl (fun arr _ => arr.push zero) block.samples
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{ real := filled, imag := Array.mk (List.replicate paddedSize zero) }
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else
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{ real := block.samples, imag := Array.mk (List.replicate n zero) }
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/-- Compute spectral energy in frequency band. -/
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def bandEnergy (freq : FrequencyDomain) (low high : Nat) (sampleRate : Nat) : Q16_16 :=
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let n := freq.real.size
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if n = 0 then zero else
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let binWidth := sampleRate / n
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let lowBin := low / binWidth
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let highBin := high / binWidth
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let indices := List.range n |>.filter (fun i => i ≥ lowBin && i ≤ highBin)
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indices.foldl (fun acc i =>
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if i < freq.real.size && i < freq.imag.size then
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let magSq := freq.real[i]! * freq.real[i]! + freq.imag[i]! * freq.imag[i]!
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acc + magSq
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else acc) zero
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/-- Detect spectral redundancy: compare band energy to total. -/
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def spectralRedundancy (bandEnergy totalEnergy : Q16_16) : Q16_16 :=
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if totalEnergy = zero then zero
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else div bandEnergy totalEnergy
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §3 DSP-Aware Compression
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Compression decision based on spectral analysis. -/
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inductive CompressionMode where
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| lossless -- No compression (unique spectral content)
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| lossy8 -- 8-bit quantization
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| lossy4 -- 4-bit quantization
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| spectral -- Spectral redundancy coding
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| genetic -- Genetic compression (DNA/Protein field-guided encoding)
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deriving Repr, DecidableEq
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/-- DSP compression parameters with curvature coupling from self-compression and genomic field. -/
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structure DspCompressionParams where
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sampleRate : Nat
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blocksize : Nat
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quantizationBits : Nat -- 8, 4, or 1
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spectralThreshold : Q16_16 -- Redundancy threshold
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kappaSquared : Q16_16 -- Curvature coupling κ² from self-compression (arXiv:2301.13142)
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-- Genomic field parameters for genetic compression
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rhoSeq : Q16_16 -- ρ_seq²: sequence alignment accuracy
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vEpigenetic : Q16_16 -- v_epigenetic²: methylation dynamics
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tauStructure : Q16_16 -- τ_structure²: 3D folding tension
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sigmaEntropy : Q16_16 -- σ_entropy²: nucleotide diversity
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qConservation : Q16_16 -- q_conservation²: evolutionary constraint
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kappaHierarchy : Q16_16 -- κ_hierarchy²: chromatin levels
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epsilonMutation : Q16_16 -- ε_mutation: mutation rate
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deriving Repr
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/-- Analyze spectral bands and select compression mode with curvature and genomic awareness.
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κ² modulates the spectral threshold: higher curvature = more aggressive compression.
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Genomic field parameters enable genetic compression when data shows sequence-like structure.
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This connects self-compression geometric structure and genomic field to DSP spectral decisions. -/
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def selectCompressionMode
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(block : SampleBlock)
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(params : DspCompressionParams) : CompressionMode :=
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let freq := computeFFT block
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let totalEnergy := bandEnergy freq 0 (params.sampleRate / 2) params.sampleRate
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let lowBandEnergy := bandEnergy freq 0 (params.sampleRate / 4) params.sampleRate
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let redundancy := spectralRedundancy lowBandEnergy totalEnergy
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-- Curvature-aware threshold: κ² increases effective threshold (more compression)
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let curvatureFactor := Q16_16.one + params.kappaSquared
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let adjustedThreshold := mul params.spectralThreshold curvatureFactor
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-- Genomic field strength: sum of genomic parameters
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let genomicStrength := params.rhoSeq + params.vEpigenetic + params.tauStructure +
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params.sigmaEntropy + params.qConservation
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let hierarchyFactor := Q16_16.one + params.kappaHierarchy
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let genomicWeight := mul genomicStrength hierarchyFactor
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-- Genetic compression enabled when genomic field is strong enough
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if genomicWeight > (ofNat 100) then
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CompressionMode.genetic
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else if redundancy > adjustedThreshold then
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CompressionMode.spectral
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else if totalEnergy < (ofNat 100) then -- Low energy = simple signal
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CompressionMode.lossy4
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else
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CompressionMode.lossless
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/-- Compress sample block using selected mode with curvature and genomic-aware compression ratio.
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κ² increases compression ratio for curved manifolds (quantization structure).
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Genomic field parameters enable DNA/Protein-like compression with hierarchy-aware encoding. -/
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def compressBlock
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(block : SampleBlock)
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(params : DspCompressionParams)
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(mode : CompressionMode) : Nat × Q16_16 :=
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let originalSize := block.samples.size * 2 -- 2 bytes per Q16_16
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-- Curvature increases compression ratio for spectral mode
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let curvatureBoost := Q16_16.one + params.kappaSquared
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-- Genomic field denominator for genetic compression
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let kappaSq := params.kappaHierarchy * params.kappaHierarchy
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let geomTerm := Q16_16.one + kappaSq
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let mutTerm := Q16_16.one + params.epsilonMutation
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let genomicDenom := mul geomTerm mutTerm
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let genomicNumerator := params.rhoSeq + params.vEpigenetic + params.tauStructure +
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params.sigmaEntropy + params.qConservation
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let genomicWeight := div genomicNumerator genomicDenom
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match mode with
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| CompressionMode.lossless => (originalSize, Q16_16.one)
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| CompressionMode.lossy8 => (originalSize / 2, ofNat 2)
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| CompressionMode.lossy4 => (originalSize / 4, ofNat 4)
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| CompressionMode.spectral =>
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let baseRatio := ofNat 8
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let adjustedRatio := mul baseRatio curvatureBoost
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(originalSize / 8, adjustedRatio)
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| CompressionMode.genetic =>
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let baseRatio := ofNat 12 -- Higher base ratio for genetic compression
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let genomicBoost := Q16_16.one + genomicWeight
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let adjustedRatio := mul baseRatio genomicBoost
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(originalSize / 12, adjustedRatio)
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §4 FPGA DSP Slice Integration
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- FPGA DSP opcode mapping (from substrate_isa_spec.md). -/
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inductive FpgaDspOpcode where
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| resonate -- 0x14: TSM_RESONATE / PHONON_LOCK (Phi=1.618)
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| mergeModes -- 0x42: TSM_MERGE_MODES
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| ingestVib -- 0x47: TSM_INGEST_VIBRATION
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deriving Repr, DecidableEq
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/-- DSP slice configuration for compression. -/
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structure DspSliceConfig where
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opcode : FpgaDspOpcode
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phi : Q16_16 -- Resonance parameter (1.618 for golden ratio)
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clockCycles : Nat -- Estimated cycles
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deriving Repr
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/-- Map compression mode to FPGA DSP opcode. -/
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def modeToOpcode (mode : CompressionMode) : FpgaDspOpcode :=
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match mode with
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| CompressionMode.spectral => FpgaDspOpcode.resonate
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| CompressionMode.genetic => FpgaDspOpcode.resonate -- Genetic compression uses resonance (phi-encoding)
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| CompressionMode.lossless => FpgaDspOpcode.mergeModes
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| _ => FpgaDspOpcode.ingestVib
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/-- Estimate DSP slice energy cost (cycles * power). -/
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def dspEnergyCost (config : DspSliceConfig) : Q16_16 :=
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let baseCost := ofNat config.clockCycles
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let phiWeight := config.phi
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mul baseCost phiWeight
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §5 Energy-Aware Decompression Scheduling
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Decompression task with energy cost and self-compression curvature. -/
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structure DecompressionTask where
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blockId : Nat
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mode : CompressionMode
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energyCost : Q16_16 -- DSP energy cost
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kappaSquared : Q16_16 -- Curvature coupling from self-compression (arXiv:2301.13142)
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priority : Nat -- Lower = higher priority
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deriving Repr
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/-- Combined cost: DSP energy + curvature penalty from self-compression.
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Higher κ² = higher structural complexity = higher scheduling priority. -/
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def combinedCost (task : DecompressionTask) : Q16_16 :=
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let curvaturePenalty := mul task.kappaSquared (ofNat 1000) -- Scale κ² to energy units
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task.energyCost + curvaturePenalty
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/-- Energy-aware scheduler: high-cost (energy + curvature) blocks first.
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This integrates self-compression geometric structure into scheduling decisions. -/
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def scheduleDecompression (tasks : Array DecompressionTask) : Array DecompressionTask :=
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tasks.qsort (fun t1 t2 => combinedCost t1 > combinedCost t2)
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §6 Theorems
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Claim boundary: computed sample energy should be non-negative. -/
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def energyNonneg (block : SampleBlock) : Prop :=
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computeEnergy block ≥ zero
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/-- Claim boundary: computed sample variance should be non-negative. -/
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def varianceNonneg (block : SampleBlock) : Prop :=
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computeVariance block ≥ zero
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/-- Claim boundary: spectral redundancy should stay in [0, 1]. -/
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def redundancyBounded (band total : Q16_16) (_hPos : total > zero) : Prop :=
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zero ≤ spectralRedundancy band total ∧ spectralRedundancy band total ≤ Q16_16.one
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/-- Claim boundary: compression ratio should be ≥ 1 (no expansion). -/
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def compressionRatioAtLeastOne
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(block : SampleBlock)
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(params : DspCompressionParams)
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(mode : CompressionMode) : Prop :=
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Q16_16.one ≤ (compressBlock block params mode).2
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/-- Claim boundary: combined cost should be non-negative (energy + curvature penalty). -/
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def combinedCostNonneg (task : DecompressionTask) : Prop :=
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combinedCost task ≥ zero
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/-- Claim boundary: higher κ² should increase scheduling priority (combined cost). -/
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def curvatureIncreasesPriority
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(task : DecompressionTask)
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(kappa1 kappa2 : Q16_16)
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(_h : kappa1 > kappa2) : Prop :=
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combinedCost { task with kappaSquared := kappa1 } >
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combinedCost { task with kappaSquared := kappa2 }
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §8 Swarm Design Review Integration
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Extract geometric parameters from DSP compression params for swarm review. -/
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def extractGeometricParams (params : DspCompressionParams) : GeometricParameters :=
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{ kappaSquared := params.kappaSquared,
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rhoSeq := params.rhoSeq,
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vEpigenetic := params.vEpigenetic,
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tauStructure := params.tauStructure,
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sigmaEntropy := params.sigmaEntropy,
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qConservation := params.qConservation,
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kappaHierarchy := params.kappaHierarchy,
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epsilonMutation := params.epsilonMutation }
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/-- Run swarm design review on compression parameters.
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Returns swarm analysis with consensus and recommendations for improvement. -/
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def runSwarmDesignReview (params : DspCompressionParams) : SwarmState :=
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let geomParams := extractGeometricParams params
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let swarm := initializeSwarm
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runSwarmAnalysis swarm geomParams
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/-- Apply swarm recommendations to improve compression parameters.
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This function interprets swarm consensus and adjusts parameters accordingly. -/
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def applySwarmRecommendations (params : DspCompressionParams) (swarm : SwarmState) : DspCompressionParams :=
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-- If consensus is low (< 0.5), increase geometric parameters
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if swarm.consensus < (ofNat 32768) then -- 0.5 in Q16.16
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-- Boost all geometric parameters to improve utilization
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{ params with
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kappaSquared := mul params.kappaSquared (ofNat 150) -- 1.5x boost
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kappaHierarchy := mul params.kappaHierarchy (ofNat 150)
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epsilonMutation := mul params.epsilonMutation (ofNat 150)
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rhoSeq := mul params.rhoSeq (ofNat 120)
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vEpigenetic := mul params.vEpigenetic (ofNat 120)
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tauStructure := mul params.tauStructure (ofNat 120)
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sigmaEntropy := mul params.sigmaEntropy (ofNat 120)
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qConservation := mul params.qConservation (ofNat 120)
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}
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else
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-- Parameters are well-tuned, keep as-is
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params
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §9 FAMM Integration (Frustration-Aware Manifold Memory)
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- FAMM-aware compression parameters with frustration-based timing. -/
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structure FammCompressionParams where
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baseParams : DspCompressionParams
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-- FAMM timing parameters
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torsionalStress : Q16_16 -- Σ²: torsional stress from manifold state
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interlockingEnergy : Q16_16 -- I_lock: interlocking energy
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laplacianEnergy : Q16_16 -- Δϕ: Hodge-Laplacian vibration energy
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deriving Repr
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/-- Derive FAMM timing from compression geometric parameters.
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Maps κ² to torsional stress and κ_hierarchy² to interlocking energy. -/
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def deriveFammTiming (params : DspCompressionParams) : FammCompressionParams :=
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-- Map curvature κ² to torsional stress (higher curvature = more stress)
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let torsionalStress := params.kappaSquared
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-- Map hierarchy κ² to interlocking energy (hierarchy depth = lock strength)
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let kappaSq := params.kappaHierarchy * params.kappaHierarchy
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let interlockingEnergy := div kappaSq (Q16_16.one + kappaSq)
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-- Map spectral redundancy to laplacian energy (redundancy = neighbor vibration)
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let laplacianEnergy := params.spectralThreshold
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{
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baseParams := params,
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torsionalStress := torsionalStress,
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interlockingEnergy := interlockingEnergy,
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laplacianEnergy := laplacianEnergy
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}
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/-- Compress with FAMM-aware timing adjustments.
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Uses FAMM timing to modulate compression mode selection and ratios. -/
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def compressBlockFamm
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(block : SampleBlock)
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(fammParams : FammCompressionParams) : Nat × Q16_16 × ManifoldTiming :=
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let timing := deriveTiming
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{ phi := zero,
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x_pos := { x := fammParams.interlockingEnergy, y := zero },
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x0_pos := { x := zero, y := zero },
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g := { xx := zero, xy := zero, yy := zero },
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t := { t1_12 := fammParams.torsionalStress, t2_12 := zero },
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a := { xx := Q16_16.one, xy := zero, yy := zero }
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} fammParams.laplacianEnergy
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let params := fammParams.baseParams
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let originalSize := block.samples.size * 2
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-- FAMM-aware compression: adjust parameters based on timing
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let adjustedParams :=
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-- If tTCL is high (high stress), use more aggressive compression
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if timing.tcl > (ofNat 22) then -- Above baseline
|
||
{ params with
|
||
kappaSquared := mul params.kappaSquared (ofNat 120), -- Boost κ²
|
||
spectralThreshold := mul params.spectralThreshold (ofNat 110) -- Tighten threshold
|
||
}
|
||
-- If tMRE is high (slipping manifold), refresh more aggressively
|
||
else if timing.mre > (ofNat 131072) then -- Above baseline
|
||
{ params with
|
||
kappaHierarchy := mul params.kappaHierarchy (ofNat 120) -- Boost hierarchy
|
||
}
|
||
else
|
||
params
|
||
|
||
let mode := selectCompressionMode block adjustedParams
|
||
let (compressedSize, ratio) := compressBlock block adjustedParams mode
|
||
|
||
(compressedSize, ratio, timing)
|
||
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
-- §7 Verification Examples
|
||
-- ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
def exampleBlock : SampleBlock :=
|
||
{ samples := #[Q16_16.one, two, Q16_16.one, two, Q16_16.one, two, Q16_16.one, two],
|
||
sampleRate := 48000 }
|
||
|
||
def exampleParams : DspCompressionParams :=
|
||
{ sampleRate := 48000,
|
||
blocksize := 8,
|
||
quantizationBits := 8,
|
||
spectralThreshold := ofNat 50, -- 50% redundancy threshold
|
||
kappaSquared := zero,
|
||
rhoSeq := zero,
|
||
vEpigenetic := zero,
|
||
tauStructure := zero,
|
||
sigmaEntropy := zero,
|
||
qConservation := zero,
|
||
kappaHierarchy := zero,
|
||
epsilonMutation := zero }
|
||
|
||
#eval computeEnergy exampleBlock
|
||
-- Expected: 8 * (1² + 2²) = 8 * 5 = 40 (in Q16.16)
|
||
|
||
#eval! computeMean exampleBlock
|
||
-- Expected: (1+2+1+2+1+2+1+2)/8 = 12/8 = 1.5
|
||
|
||
#eval! computeVariance exampleBlock
|
||
-- Expected: E[x²] - E[x]² = 5 - 2.25 = 2.75
|
||
|
||
#eval! selectCompressionMode exampleBlock exampleParams
|
||
-- Expected: spectral (high redundancy in repeating pattern)
|
||
|
||
#eval! compressBlock exampleBlock exampleParams CompressionMode.lossy4
|
||
-- Expected: (16/4 = 4 bytes, 4.0 ratio)
|
||
|
||
#eval modeToOpcode CompressionMode.spectral
|
||
-- Expected: resonate
|
||
|
||
#eval dspEnergyCost { opcode := FpgaDspOpcode.resonate, phi := ofNat 16180, clockCycles := 100 }
|
||
-- Expected: 100 * 1.618 ≈ 161.8
|
||
|
||
end Semantics.StreamCompression
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