Research-Stack/5-Applications/scripts/execute_swarm_dsp_reconfiguration.py

276 lines
9.1 KiB
Python

#!/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()