#!/usr/bin/env python3 """ Swarm Task: Reconfigure DSP Concept via Morphic Scalar This script assigns the network swarm to reconfigure the concept of DSP (Digital Signal Processing) from fixed-function hardware to morphic-scalar- controlled reconfigurable processing units. Key changes: - DSP slices are reconfigurable via morphic scalar state machine - OEPI threshold determines DSP allocation priority - DSP modes adapt to signal characteristics - Integration with FPGA optimization (5 DSP slices) """ import sys import os import json import logging from datetime import datetime # Ensure project root is in path sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) from infra.lean_unified_shim import LeanUnifiedShim logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger("SwarmDSPReconfiguration") class SwarmDSPReconfiguration: """ Swarm task to reconfigure DSP concept via morphic scalar. """ def __init__(self, lean_path="0-Core-Formalism/lean/Semantics"): self.shim = LeanUnifiedShim(lean_path) def analyze_current_dsp_concept(self): """Analyze current DSP concept in Lean codebase.""" logger.info("Analyzing current DSP concept...") # Query Lean for DSP-related modules result = self.shim.query_lean(""" import Semantics.Semantics.DSPTranslation import Semantics.Semantics.DspErasureCoding -- Return DSP module information { "dsp_translation": { "module": "DSPTranslation", "purpose": "DSP to neuromorphic formal bridge", "features": ["Q16.16 fixed-point", "STDP learning", "geodesic cost"] }, "dsp_erasure_coding": { "module": "DspErasureCoding", "purpose": "DSP-aware 3-stream erasure coding", "features": ["3-stream redundancy", "spectral analysis", "FPGA DSP integration"] } } """) return result def propose_morphic_dsp_concept(self): """Propose new morphic-scalar-based DSP concept.""" logger.info("Proposing morphic-scalar-based DSP concept...") proposal = { "concept_name": "MorphicDSP", "core_principle": "DSP as reconfigurable processing unit controlled by morphic scalar", "key_changes": [ "DSP slices are not fixed multipliers but reconfigurable", "Morphic scalar state machine controls DSP configuration", "OEPI threshold determines DSP allocation priority", "DSP modes adapt to signal characteristics via scalar collapse" ], "dsp_modes": [ "multiply - Standard multiplication", "accumulate - Accumulation for dot products", "convolution - Convolution kernel", "fft - FFT butterfly operations", "filter - Digital filtering", "adaptive - Adaptive filtering (OEPI-controlled)" ], "state_to_mode_mapping": { "superposed": "adaptive", "scouting": "filter", "measureLocalNeed": "convolution", "collapsedProfile": "multiply", "execute": "accumulate", "queryCollective": "fft", "operatorAlert": "adaptive", "lowPowerPassiveMode": "filter" }, "oepi_allocation": { "critical (≥95)": "5 DSP slices", "medium (70-95)": "3 DSP slices", "low (<70)": "1 DSP slice" }, "fpga_integration": { "total_slices": 5, "utilization": "62.5% of 8 available on iCE40 HX8K", "optimization": "Parallel OEPI calculation uses 5 DSP slices" } } return proposal def generate_lean_morphic_dsp(self): """Generate Lean code for MorphicDSP module.""" logger.info("Generating Lean code for MorphicDSP module...") lean_code = """ /- Copyright (c) 2026 Sovereign Research Stack. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Research Stack Team MorphicDSP.lean — Reconfigurable DSP via Morphic Scalar This module reconfigures the concept of DSP (Digital Signal Processing) from fixed-function hardware to morphic-scalar-controlled reconfigurable processing. -/ import Mathlib.Data.Nat.Basic import Mathlib.Data.Fin.Basic import Semantics.FixedPoint import Semantics.Morphic import Semantics.OEPI namespace Semantics.MorphicDSP open Semantics.Q16_16 /-- DSP operation mode (reconfigurable via morphic scalar). -/ inductive DspMode where | multiply -- Standard multiplication | accumulate -- Accumulation for dot products | convolution -- Convolution kernel | fft -- FFT butterfly operations | filter -- Digital filtering | adaptive -- Adaptive filtering (OEPI-controlled) deriving Repr, DecidableEq, BEq /-- DSP slice configuration. -/ structure DspConfig where mode : DspMode operandA : Q16_16 operandB : Q16_16 accumulator : Q16_16 oepiThreshold : Q16_16 deriving Repr /-- DSP slice state (controlled by morphic scalar). -/ structure DspSlice where sliceId : Nat config : DspConfig active : Bool morphicState : Morphic.ScalarState deriving Repr /-- Map morphic scalar state to DSP mode. -/ def stateToDspMode (state : Morphic.ScalarState) : DspMode := match state with | Morphic.ScalarState.superposed => DspMode.adaptive | Morphic.ScalarState.scouting => DspMode.filter | Morphic.ScalarState.measureLocalNeed => DspMode.convolution | Morphic.ScalarState.collapsedProfile => DspMode.multiply | Morphic.ScalarState.execute => DspMode.accumulate | Morphic.ScalarState.queryCollective => DspMode.fft | Morphic.ScalarState.operatorAlert => DspMode.adaptive | Morphic.ScalarState.lowPowerPassiveMode => DspMode.filter | _ => DspMode.multiply /-- Configure DSP slice based on morphic scalar state and OEPI. -/ def configureDspSlice (slice : DspSlice) (oepi : Q16_16) : DspSlice := let mode := stateToDspMode slice.morphicState let adaptiveThreshold := if mode = DspMode.adaptive then oepi else zero let newConfig := { slice.config with mode := mode, oepiThreshold := adaptiveThreshold } { slice with config := newConfig, active := true /-- DSP slice bank (5 slices for morphic scalar FPGA). -/ structure DspBank where slices : Array DspSlice totalSlices : Nat activeSlices : Nat deriving Repr /-- Initialize DSP bank with 5 slices. -/ def initDspBank : DspBank := let slices := (List.range 5).map (fun i => { sliceId := i, config := { mode := DspMode.multiply, operandA := zero, operandB := zero, accumulator := zero, oepiThreshold := zero }, active := false, morphicState := Morphic.ScalarState.superposed } ) { slices := slices.toArray, totalSlices := 5, activeSlices := 0 } /-- Allocate DSP slices based on OEPI threshold. -/ def allocateDspSlices (bank : DspBank) (oepi : Q16_16) : DspBank := let criticalThreshold := Q16_16.ofInt 95 let mediumThreshold := Q16_16.ofInt 70 let allocationCount := if oepi >= criticalThreshold then 5 else if oepi >= mediumThreshold then 3 else 1 let updatedSlices := bank.slices.mapIdx (fun i slice => if i < allocationCount then { slice with active := true } else { slice with active := false } ) { bank with slices := updatedSlices, activeSlices := allocationCount } end Semantics.MorphicDSP """ return lean_code def execute_reconfiguration(self): """Execute DSP reconfiguration task.""" logger.info("Executing DSP reconfiguration task...") # Step 1: Analyze current DSP concept current_dsp = self.analyze_current_dsp_concept() logger.info(f"Current DSP analysis: {current_dsp}") # Step 2: Propose morphic DSP concept proposal = self.propose_morphic_dsp_concept() logger.info(f"Morphic DSP proposal: {proposal}") # Step 3: Generate Lean code lean_code = self.generate_lean_morphic_dsp() logger.info("Lean code generated for MorphicDSP module") # Step 4: Save results timestamp = datetime.now().isoformat() result = { "task": "swarm_dsp_reconfiguration", "timestamp": timestamp, "current_dsp_analysis": current_dsp, "morphic_dsp_proposal": proposal, "lean_code": lean_code, "status": "complete" } output_path = "shared-data/data/swarm_dsp_reconfiguration_result.json" with open(output_path, 'w') as f: json.dump(result, f, indent=2) logger.info(f"Results saved to {output_path}") return result def main(): """Main execution.""" task = SwarmDSPReconfiguration() result = task.execute_reconfiguration() print(json.dumps(result, indent=2)) if __name__ == "__main__": main()