Compiler Intelligence Guide¶
SC-NeuroCore includes 59 compiler intelligence features that transform the compiler from a passive code generator into an active decision-making system. This guide covers formal verification, target selection, deployment analysis, extensibility, security, reliability, and frontier co-design capabilities.
Table of Contents¶
- Formal Assurance and Provenance (§26–§33)
- Target Selection and Topology Planning (§34–§41)
- Full-Stack Deployment Analysis (§42–§51)
- Extensibility and Reporting (§52–§59)
- Quick Reference Table
- Usage Patterns
Formal Assurance and Provenance (§26–§33)¶
§26. Formal Equivalence Sketch — generate_equivalence_sketch()¶
Generates a formal equivalence checking sketch (SAT-based) to verify that the compiled Verilog exactly matches the original ODE semantics within fixed-point tolerance bounds.
from sc_neurocore.compiler.intelligence import generate_equivalence_sketch
sketch = generate_equivalence_sketch(
equations={"v": "-(v - v_rest) / tau + I"},
data_width=16, fraction=8,
)
# sketch contains SystemVerilog assertions + SAT constraints
print(sketch[:200])
Use case: Post-compilation formal verification. Proves bit-accurate equivalence between Python model and generated RTL.
§27. Multi-Timescale Partitioner — partition_timescales()¶
Separates fast (membrane dynamics, ~1ms) from slow (synaptic plasticity, ~100ms) variables, enabling heterogeneous clock domains and power savings.
from sc_neurocore.compiler.intelligence import partition_timescales
result = partition_timescales(
equations={"v": "fast dynamics", "w": "slow adaptation"},
time_constants={"v": 1.0, "w": 100.0},
threshold_ratio=10.0,
)
print(result.fast_partition) # ['v']
print(result.slow_partition) # ['w']
Use case: Power reduction via clock gating slow variables.
§28. Provenance Chain — generate_provenance_chain()¶
Creates a cryptographic audit trail from model definition through compilation to deployment, enabling full traceability for safety-critical applications.
from sc_neurocore.compiler.intelligence import generate_provenance_chain
chain = generate_provenance_chain(
equations={"v": "-(v)/tau + I"},
profile_name="artix7",
author="lab_name",
)
print(chain.chain_hash) # SHA-256 of full pipeline
print(chain.stages) # list of provenance records
Use case: DO-254 audit compliance, reproducibility.
§29. Compliance Matrix — generate_compliance_matrix()¶
Auto-generates a certification compliance matrix mapping SC-NeuroCore features to regulatory requirements (DO-254, IEC 61508, ISO 26262).
from sc_neurocore.compiler.intelligence import generate_compliance_matrix
matrix = generate_compliance_matrix(
standard="DO-254",
equations={"v": "expr"},
profile_name="artix7",
)
for req in matrix.requirements[:3]:
print(f"{req['id']}: {req['status']}")
§30. Energy Schedule — generate_energy_schedule()¶
Generates a time-domain power schedule for duty-cycled operation, including sleep/wake transitions and energy budgeting.
from sc_neurocore.compiler.intelligence import generate_energy_schedule
sched = generate_energy_schedule(
power_active_mw=100.0,
power_sleep_mw=0.1,
duty_cycle=0.3,
)
print(f"Average: {sched.avg_power_mw} mW")
§31. Side-Channel Lint — lint_side_channels()¶
Detects potential side-channel leakage (timing, power) in neuron implementations that could compromise model IP or cryptographic parameters.
from sc_neurocore.compiler.intelligence import lint_side_channels
warnings = lint_side_channels(
equations={"v": "-(v - v_rest) / tau + I * weight"},
)
for w in warnings.findings:
print(w) # e.g., "data-dependent branch on 'weight'"
§32. Drift Compensation — generate_drift_compensator()¶
Generates analog drift compensation logic for memristive and ferroelectric devices where synaptic weights degrade over time.
from sc_neurocore.compiler.intelligence import generate_drift_compensator
verilog = generate_drift_compensator(
num_weights=256,
drift_model="logarithmic",
)
print(verilog[:200]) # Verilog refresh controller
§33. Heterogeneous Dispatch — plan_heterogeneous_dispatch()¶
Plans multi-target compilation where different neuron populations execute on different hardware platforms simultaneously (e.g., sensory on FPGA, cortical on neuromorphic, motor on MCU).
from sc_neurocore.compiler.intelligence import plan_heterogeneous_dispatch
plan = plan_heterogeneous_dispatch(
populations={"sensory": "artix7", "cortical": "loihi2", "motor": "rp2040"},
connections=[("sensory", "cortical"), ("cortical", "motor")],
)
for p, target in plan.assignments.items():
print(f"{p} → {target}")
Target Selection and Topology Planning (§34–§41)¶
§34. Auto-Target Recommender — recommend_target()¶
Given a set of constraints (power budget, latency, cost), automatically recommends the optimal hardware platform from all 175 profiles.
from sc_neurocore.compiler.intelligence import recommend_target
recs = recommend_target(
constraints={"max_power_mw": 500, "min_freq_mhz": 100, "max_width": 16},
top_k=5,
)
for r in recs:
print(f"{r['name']} (score: {r['score']:.2f})")
Use case: "Just tell me what chip to use for my model."
§35. Partial Reconfiguration Planner — plan_partial_reconfiguration()¶
Plans FPGA Dynamic Partial Reconfiguration (DPR) to time-multiplex different SNN layers on the same fabric, reducing area by 2-4×.
from sc_neurocore.compiler.intelligence import plan_partial_reconfiguration
plan = plan_partial_reconfiguration(
regions={"region_a": 500, "region_b": 300},
total_luts=20000,
)
print(plan.schedule)
§36. Supply Chain Risk Scorer — score_supply_chain_risk()¶
Profiles geopolitical, sole-source, and ITAR risk for each hardware target, enabling informed procurement decisions.
from sc_neurocore.compiler.intelligence import score_supply_chain_risk
risk = score_supply_chain_risk("loihi2")
print(f"Risk: {risk.overall_risk}")
print(f"Factors: {risk.risk_factors}")
§37. Bit-True Simulation Kernel — generate_bittrue_kernel()¶
Generates C or Rust source code that produces bit-identical output to the Verilog RTL, enabling software-in-the-loop (SIL) verification.
from sc_neurocore.compiler.intelligence import generate_bittrue_kernel
code = generate_bittrue_kernel(
equations={"v": "-(v - v_rest) / tau + I"},
data_width=16, fraction=8,
language="c",
)
print(code[:300]) # C source with fixed-point arithmetic
§38. Model Complexity Classifier — classify_model_complexity()¶
Classifies a model as memory-bound, compute-bound, or communication-bound, then routes to the optimal paradigm (PIM, FPGA, or optical).
from sc_neurocore.compiler.intelligence import classify_model_complexity
cls = classify_model_complexity(
equations={"v": "a*b + c*d", "u": "e*f"},
num_neurons=10000,
num_synapses=1000000,
)
print(cls.classification) # "memory_bound"
print(cls.recommended_class) # "in_memory"
§39. Cross-Compilation Cache — CompilationCache¶
Memoized compilation cache enabling O(1) retrieval of repeated compilations.
from sc_neurocore.compiler.intelligence import CompilationCache
cache = CompilationCache()
cache.store("lif_artix7", {"verilog": "...", "timing": "..."})
hit = cache.lookup("lif_artix7")
print(hit is not None) # True
§40. Thermal Envelope Estimator — estimate_thermal_envelope()¶
Predicts junction temperature from power dissipation using thermal resistance models, catching thermal violations at compile time.
from sc_neurocore.compiler.intelligence import estimate_thermal_envelope
t = estimate_thermal_envelope(power_mw=500, theta_ja=25.0, t_ambient=40.0)
print(f"T_j = {t.t_junction}°C, Margin = {t.thermal_margin}°C")
print(f"Status: {t.pass_fail}")
§41. Network Topology Optimizer — optimize_network_topology()¶
Minimizes inter-chip spike bandwidth in multi-chip SNN deployments using graph partitioning.
from sc_neurocore.compiler.intelligence import optimize_network_topology
result = optimize_network_topology(
adjacency={"a": ["b", "c"], "b": ["c"], "c": ["a"]},
num_chips=2,
)
print(f"Bandwidth reduction: {result.bandwidth_reduction:.1%}")
Full-Stack Deployment Analysis (§42–§51)¶
§42. NIR / ONNX-SNN Import — import_nir_graph()¶
Imports trained SNN models from snnTorch, Norse, Sinabs, or Nengo via the Neuromorphic Intermediate Representation (NIR) standard, bridging the gap between ML training frameworks and hardware compilation.
from sc_neurocore.compiler.intelligence import import_nir_graph
# NIR graph from snnTorch export
nir_data = {
"nodes": {
"layer0": {"type": "LIF", "tau": 20.0},
"layer1": {"type": "LIF", "tau": 10.0},
},
"edges": [["layer0", "layer1"]],
}
graph = import_nir_graph(nir_data, framework="snnTorch")
print(graph.equations) # ODE equations ready for compilation
Use case: Train in PyTorch/snnTorch → export NIR → compile to any of 165 targets.
§43. ODE Stability Verifier — verify_ode_stability()¶
Verifies that the discretized ODE system is numerically stable using eigenvalue analysis of the linearized system. Prevents divergent hardware.
from sc_neurocore.compiler.intelligence import verify_ode_stability
result = verify_ode_stability(
equations={"v": "-(v)/tau + I", "u": "a*(b*v - u)"},
dt=0.1,
time_constants={"v": 10.0, "u": 100.0},
)
print(f"Stable: {result.stable}")
print(f"Critical dt: {result.critical_dt} ms")
print(f"Max eigenvalue: {result.max_eigenvalue}")
Mathematical basis: Forward Euler stability region |1 + λ·dt| < 1, where λ = -1/τ for each linearized ODE.
§44. Power Intent Generator — generate_power_intent()¶
Generates IEEE 1801 Unified Power Format (UPF) for multi-voltage neuron arrays, including power domains, isolation cells, and retention strategies.
from sc_neurocore.compiler.intelligence import generate_power_intent
upf = generate_power_intent("sc_lif_array", num_domains=4, always_on=True)
# Write to file for use in Synopsys/Cadence flow
with open("sc_lif_array.upf", "w") as f:
f.write(upf)
Output includes: create_power_domain, set_isolation, set_retention,
add_power_state — compatible with all major EDA tools.
§45. Carbon Footprint Estimator — estimate_carbon_footprint()¶
Estimates lifecycle CO₂ emissions (manufacturing + operation) per compilation target, enabling ESG-compliant hardware selection.
from sc_neurocore.compiler.intelligence import estimate_carbon_footprint
# Compare carbon impact across targets
for target in ["artix7", "loihi2", "finalspark_neuroplatform"]:
c = estimate_carbon_footprint(target, power_mw=100)
print(f"{target}: {c.total_5yr_kg_co2:.1f} kg CO₂ (5yr)")
Regulatory context: EU carbon labelling becomes mandatory in 2027. SC-NeuroCore is the only neuromorphic compiler that generates this data.
§46. Debug Probe Inserter — insert_debug_probes()¶
Auto-inserts ILA (Xilinx) or SignalTap (Intel) debug probes for post-silicon debugging, with vendor-specific TCL commands.
from sc_neurocore.compiler.intelligence import insert_debug_probes
probes = insert_debug_probes(
"sc_lif", {"v": "expr", "u": "expr"},
vendor="xilinx", depth=4096,
)
print(probes.signals) # ['v', 'u', 'spike_out', 'clk', 'rst_n']
print(probes.tcl_commands) # Vivado TCL script
§47. Memory Map Generator — generate_memory_map()¶
Generates address decoders for multi-neuron SoC arrays, producing both the address map specification and synthesisable Verilog.
from sc_neurocore.compiler.intelligence import generate_memory_map
mmap = generate_memory_map(
"sc_lif_array",
{"v": "expr", "u": "expr", "I": "expr"},
num_neurons=1024, data_width=16,
base_address=0x4000_0000,
)
print(f"Total address space: {mmap.total_bytes} bytes")
print(mmap.decoder_verilog)
§48. Model Portability Scorer — score_portability()¶
Scores 0–100 how portable a neuron model is across all 175 profiles, identifying blockers that limit cross-platform deployment.
from sc_neurocore.compiler.intelligence import score_portability
# Simple LIF — highly portable
s = score_portability({"v": "-(v - v_rest) / tau + I"})
print(f"Score: {s.score}/100 ({s.compatible_profiles}/{s.total_profiles})")
# Complex Hodgkin-Huxley — limited portability
s = score_portability({"v": "g_na*m*m*m*h*(E_na-v) + g_k*n*n*n*n*(E_k-v)"})
print(f"Score: {s.score}/100, Blockers: {s.blockers}")
§49. Aging/Reliability Predictor — predict_reliability()¶
Predicts Mean Time To Failure (MTTF) using Arrhenius temperature acceleration and voltage stress models.
from sc_neurocore.compiler.intelligence import predict_reliability
r = predict_reliability(voltage_v=0.9, temperature_c=85, node_nm=7)
print(f"MTTF: {r.mttf_years:.1f} years")
print(f"Failure mode: {r.failure_mode}") # TDDB, HCI, or NBTI
print(f"Temp acceleration: {r.temp_accel:.1f}×")
Physics: Uses Ea=0.7eV Arrhenius model with cubic voltage acceleration.
§50. Fault Tree Generator — generate_fault_tree()¶
Auto-generates Fault Tree Analysis (FTA) and FMEA for DO-254 Level A and ISO 26262 ASIL-D certification.
from sc_neurocore.compiler.intelligence import generate_fault_tree
ft = generate_fault_tree("sc_lif", {"v": "expr", "u": "expr"})
print(f"Top event: {ft.top_event}")
print(f"Basic events: {len(ft.basic_events)}")
print(f"Minimal cut sets: {len(ft.mcs)}")
for e in ft.basic_events[:3]:
print(f" {e['id']}: rate={e['rate']:.0e}")
§51. Auto-Testbench Generator — generate_testbench()¶
Generates complete Cocotb or UVM verification testbenches with reset verification and multi-cycle runtime checks.
from sc_neurocore.compiler.intelligence import generate_testbench
# Cocotb testbench
tb = generate_testbench("sc_lif", {"v": "expr"}, framework="cocotb")
with open("test_sc_lif.py", "w") as f:
f.write(tb)
# UVM testbench
tb_uvm = generate_testbench("sc_lif", {"v": "expr"}, framework="uvm")
Extensibility and Reporting (§52–§59)¶
§52. CDC Analyzer — analyze_cdc()¶
Formal clock domain crossing analysis across neuron array boundaries. Identifies unsynchronized crossings and recommends synchroniser types.
from sc_neurocore.compiler.intelligence import analyze_cdc
report = analyze_cdc(
{"v": "u + I", "u": "v - threshold"},
clock_domains={"v": "clk_fast", "u": "clk_slow"},
)
print(f"Crossings: {report.total_crossings}")
print(f"Safe: {report.safe}")
for c in report.crossings:
print(f" {c['signal']}: {c['src_domain']}→{c['dst_domain']} ({c['sync_type']})")
§53. TOML Profile Auto-Loader — load_profiles_from_toml()¶
The extensibility endgame. Users and vendors can define custom hardware
profiles in a .toml file without modifying SC-NeuroCore source code.
# my_custom_profiles.toml
[[profile]]
name = "my_asic_v2"
vendor = "InternalDesign"
family = "ASIC-v2"
platform_class = "asic"
data_width = 24
fraction = 12
overflow = "saturate"
rounding = "nearest"
max_freq_mhz = 800
notes = "Internal 24-bit ASIC for production deployment."
from sc_neurocore.compiler.intelligence import load_profiles_from_toml
from sc_neurocore.compiler.platforms import get_profile
loaded = load_profiles_from_toml("my_custom_profiles.toml")
print(loaded) # ['my_asic_v2']
p = get_profile("my_asic_v2")
print(f"{p.vendor} {p.family}: Q{p.data_width-p.fraction}.{p.fraction}")
Any future hardware platform can be added with zero code changes.
§54. Multi-Die Floorplanner — plan_multi_die_floorplan()¶
Assigns neuron blocks to chiplet/die positions using first-fit-decreasing bin packing for 3D-stacked and 2.5D chiplet architectures.
from sc_neurocore.compiler.intelligence import plan_multi_die_floorplan
result = plan_multi_die_floorplan(
blocks={"visual_cortex": 800, "motor_cortex": 600,
"prefrontal": 400, "cerebellum": 500},
die_capacity=1000, num_dies=4,
)
for block, die in result.die_assignment.items():
print(f" {block} → Die {die}")
print(f"Dies used: {result.total_dies}")
§55. Regression Watchdog — check_regression()¶
Detects performance regressions between compilations by comparing metric deltas against configurable thresholds.
from sc_neurocore.compiler.intelligence import check_regression
checks = check_regression(
baseline={"area_luts": 1200, "fmax_mhz": 250, "power_mw": 85},
current={"area_luts": 1350, "fmax_mhz": 240, "power_mw": 90},
threshold_pct=5.0,
)
for c in checks:
status = "⚠️ REGRESSED" if c.regression else "✅ OK"
print(f" {c.metric}: {c.baseline}→{c.current} ({c.delta_pct:+.1f}%) {status}")
§56. License Compliance Checker — check_license_compliance()¶
Verifies IP core licensing compatibility using an SPDX-based compatibility matrix, preventing copyleft contamination.
from sc_neurocore.compiler.intelligence import check_license_compliance
result = check_license_compliance(
project_license="Apache-2.0",
dependencies={
"numpy": "BSD-3",
"scipy": "BSD-3",
"gpl_ip_core": "GPL-3.0",
},
)
print(f"Compatible: {result.compatible}")
for c in result.conflicts:
print(f" ⚠️ {c}")
§57. Power State Machine — generate_power_state_machine()¶
Generates synthesisable Verilog FSMs for ultra-low-power operation with configurable sleep/wake/hibernate states.
from sc_neurocore.compiler.intelligence import generate_power_state_machine
verilog = generate_power_state_machine("sc_lif")
# Default states: ACTIVE → IDLE → SLEEP → HIBERNATE
with open("sc_lif_power_fsm.v", "w") as f:
f.write(verilog)
§58. Platform Discovery Hook — register_platform_hook()¶
Plugin API enabling third-party vendors to register hardware profiles at runtime without modifying SC-NeuroCore source.
from sc_neurocore.compiler.intelligence import (
register_platform_hook, discover_platforms,
)
from sc_neurocore.compiler.platforms import HardwareProfile
def vendor_x_profiles():
return [HardwareProfile(
name="vendorx_nn_v3",
vendor="VendorX", family="NN-v3",
platform_class="accelerator",
data_width=16, fraction=8,
overflow="saturate", rounding="nearest",
)]
register_platform_hook(vendor_x_profiles)
discovered = discover_platforms()
print(f"Discovered: {discovered}")
§59. Compilation Report — generate_compilation_report()¶
One-click comprehensive markdown report consolidating target info, carbon footprint, reliability prediction, and module specification.
from sc_neurocore.compiler.intelligence import generate_compilation_report
report = generate_compilation_report(
"sc_lif", {"v": "-(v)/tau + I"}, "artix7",
)
with open("compilation_report.md", "w") as f:
f.write(report)
Quick Reference Table¶
| § | Function | Category |
|---|---|---|
| 26 | generate_equivalence_sketch() |
Verification |
| 27 | partition_timescales() |
Optimisation |
| 28 | generate_provenance_chain() |
Traceability |
| 29 | generate_compliance_matrix() |
Certification |
| 30 | generate_energy_schedule() |
Power |
| 31 | lint_side_channels() |
Security |
| 32 | generate_drift_compensator() |
Reliability |
| 33 | plan_heterogeneous_dispatch() |
Deployment |
| 34 | recommend_target() |
Auto-Targeting |
| 35 | plan_partial_reconfiguration() |
FPGA |
| 36 | score_supply_chain_risk() |
Risk |
| 37 | generate_bittrue_kernel() |
Verification |
| 38 | classify_model_complexity() |
Analysis |
| 39 | CompilationCache |
Performance |
| 40 | estimate_thermal_envelope() |
Thermal |
| 41 | optimize_network_topology() |
Deployment |
| 42 | import_nir_graph() |
Import |
| 43 | verify_ode_stability() |
Verification |
| 44 | generate_power_intent() |
Power |
| 45 | estimate_carbon_footprint() |
ESG |
| 46 | insert_debug_probes() |
Debug |
| 47 | generate_memory_map() |
SoC |
| 48 | score_portability() |
Analysis |
| 49 | predict_reliability() |
Reliability |
| 50 | generate_fault_tree() |
Safety |
| 51 | generate_testbench() |
Verification |
| 52 | analyze_cdc() |
Verification |
| 53 | load_profiles_from_toml() |
Extensibility |
| 54 | plan_multi_die_floorplan() |
Deployment |
| 55 | check_regression() |
QA |
| 56 | check_license_compliance() |
Legal |
| 57 | generate_power_state_machine() |
Power |
| 58 | register_platform_hook() |
Extensibility |
| 59 | generate_compilation_report() |
Reporting |
Usage Patterns¶
Pattern 1: Train → Import → Compile → Deploy¶
# 1. Train in snnTorch (external)
# 2. Export to NIR (external)
# 3. Import into SC-NeuroCore
graph = import_nir_graph(nir_data, framework="snnTorch")
# 4. Verify stability
stability = verify_ode_stability(graph.equations, dt=0.1)
assert stability.stable
# 5. Auto-select target
recs = recommend_target(constraints={"max_power_mw": 500})
# 6. Compile + generate report
report = generate_compilation_report("my_snn", graph.equations, recs[0]["name"])
Pattern 2: Safety-Critical Certification Pipeline¶
# 1. Generate compliance matrix
matrix = generate_compliance_matrix("DO-254", equations, "artix7")
# 2. Generate fault tree
ft = generate_fault_tree("sc_lif", equations)
# 3. Generate provenance chain
chain = generate_provenance_chain(equations, "artix7", "lab_name")
# 4. Predict reliability
rel = predict_reliability(voltage_v=0.9, temperature_c=85)
# 5. Generate testbench
tb = generate_testbench("sc_lif", equations)
Pattern 3: Custom Platform Registration¶
# Option A: TOML file (no code changes)
loaded = load_profiles_from_toml("my_platforms.toml")
# Option B: Runtime hook (vendor SDK)
register_platform_hook(my_vendor_discovery_fn)
discover_platforms()
# Option C: Auto-construct from a hardware specification
from sc_neurocore.compiler.platforms import HardwareProfile
p = HardwareProfile.from_constraints("my_chip", vendor="MyVendor", max_power_budget_mw=5)
Pattern 4: Security-Hardened Deployment¶
from sc_neurocore.compiler.intelligence import (
lint_hardware_trojans, obfuscate_ip, embed_watermark,
generate_sbom, schedule_seu_scrubbing,
)
# 1. Trojan detection
trojan = lint_hardware_trojans(equations)
assert trojan.risk_level == "LOW"
# 2. IP protection
obf = obfuscate_ip("sc_lif", equations, key_length=128)
# 3. Watermark
wm = embed_watermark("sc_lif", equations, owner_id="MyLab")
# 4. Compliance
sbom = generate_sbom("sc_lif", "artix7")
# 5. Space-grade scrubbing
scrub = schedule_seu_scrubbing(1_000_000, orbit_altitude_km=800)
Security, Sovereignty, and Compliance (§60–§67)¶
§60. Hardware Trojan Lint — lint_hardware_trojans()¶
Detects suspicious combinational paths in the ODE dependency graph that could hide hardware trojans — dormant triggers and payload injection.
from sc_neurocore.compiler.intelligence import lint_hardware_trojans
result = lint_hardware_trojans(
{"v": "-(v)/tau + I", "u": "a*(b*v - u)"},
)
print(f"Risk: {result.risk_level}")
print(f"Checks: {result.total_checks}")
§61. SBOM/HBOM Generator — generate_sbom()¶
Generates CycloneDX/SPDX Bill of Materials. Required by EU CRA (2026).
from sc_neurocore.compiler.intelligence import generate_sbom
sbom = generate_sbom("sc_lif", "artix7",
dependencies={"numpy": "1.26.0"}, sbom_format="CycloneDX")
print(f"Components: {sbom.total_components}")
§62. HIL Calibration Protocol — generate_hil_calibration()¶
Hardware-in-the-loop calibration protocol for analog drift compensation. The generator emits a deterministic Latin-hypercube design matrix, repetition count, settle-cycle requirement, correction model, observable list, and acceptance tolerance so the protocol can be replayed and audited.
from sc_neurocore.compiler.intelligence import generate_hil_calibration
cal = generate_hil_calibration("sc_lif", {"v": "expr", "u": "expr"},
parameters={"tau": (5.0, 50.0)},
sample_points=10,
repetitions=3,
settle_cycles=32,
acceptance_tolerance=1 / 256,
)
for step in cal.protocol_steps:
print(step)
print(cal.sample_count, cal.design_matrix[0])
§63. Digital Twin Shadow — generate_digital_twin()¶
Generates a Python class that mirrors deployed hardware state in real-time.
from sc_neurocore.compiler.intelligence import generate_digital_twin
twin_code = generate_digital_twin("sc_lif", {"v": "-(v)/tau"}, "artix7")
# Contains: __init__, step(), compare()
§64. UCIe Protocol Mapper — map_ucie_protocol()¶
Maps neuron blocks to UCIe die-to-die protocol lanes for chiplet architectures.
from sc_neurocore.compiler.intelligence import map_ucie_protocol
mapping = map_ucie_protocol({"cortex": 256, "motor": 128})
print(f"Total bandwidth: {mapping.total_bandwidth_gbps} Gbps")
§65. SEU Scrub Scheduler — schedule_seu_scrubbing()¶
Generates scrubbing schedules for space-grade configuration memory.
from sc_neurocore.compiler.intelligence import schedule_seu_scrubbing
s = schedule_seu_scrubbing(1_000_000, orbit_altitude_km=800)
print(f"Interval: {s.interval_ms:.0f} ms, SEU rate: {s.expected_seu_rate:.4f}/day")
§66. IP Obfuscation — obfuscate_ip()¶
Logic locking + structural transformation for IP reverse-engineering protection.
from sc_neurocore.compiler.intelligence import obfuscate_ip
result = obfuscate_ip("sc_lif", {"v": "expr"}, key_length=128)
print(f"Key: {result.key_bits} bits, Methods: {result.techniques_applied}")
§67. Model Watermark — embed_watermark()¶
Embeds verifiable watermarks into compiled netlists. Survives synthesis.
from sc_neurocore.compiler.intelligence import embed_watermark
wm = embed_watermark("sc_lif", {"v": "expr"}, owner_id="MyLab")
print(f"Hash: {wm.watermark_hash}, Overhead: {wm.overhead_percent}%")
Adaptive Reliability and Telemetry (§68–§76)¶
§68. Approximate Computing Configurator — configure_approximation()¶
Trades precision for energy by identifying variables that can tolerate reduced bit-widths or aggressive truncation, generating approximation profiles per population.
from sc_neurocore.compiler.intelligence import configure_approximation
result = configure_approximation(
{"v": "-(v)/tau + I"}, max_error_pct=5.0
)
print(f"Energy savings: {result.total_energy_savings_pct}%")
§69. Energy Harvesting Modeler — model_energy_harvest()¶
Analyses the feasibility of running the compiled design on batteryless edge devices using ambient energy (solar, RF, thermal, piezoelectric).
from sc_neurocore.compiler.intelligence import model_energy_harvest
eh = model_energy_harvest(
power_mw=50.0, harvester_type="solar", environment="indoor"
)
print(f"Energy positive: {eh.energy_positive}")
print(f"Duty cycle: {eh.recommended_duty_cycle:.2f}")
§70. Aging-Aware Timing Predictor — predict_aging()¶
Predicts circuit degradation over time due to NBTI and HCI, generating derated timing bounds to ensure the hardware meets timing closure years after deployment.
from sc_neurocore.compiler.intelligence import predict_aging
aging = predict_aging(fmax_mhz=500.0, years=10, temperature_c=85.0)
print(f"Degraded Fmax: {aging.degraded_fmax_mhz} MHz")
§71. DVFS Controller Generator — generate_dvfs_controller()¶
Generates a dynamic voltage and frequency scaling (DVFS) state machine that scales performance based on network spike rate.
from sc_neurocore.compiler.intelligence import generate_dvfs_controller
dvfs = generate_dvfs_controller("sc_lif_array")
# Verilog output: module sc_lif_array_dvfs_ctrl
§72. Pareto Frontier Explorer — explore_pareto()¶
Sweeps compilation parameters to find the non-dominated set of configurations balancing power, area, and latency.
from sc_neurocore.compiler.intelligence import explore_pareto
pareto = explore_pareto({"v": "-(v)/tau + I"})
for p in pareto[:3]:
print(f"P: {p.power_mw}mW, A: {p.area_luts}LUTs, L: {p.latency_ns}ns")
§73. Post-Quantum IP Protector — protect_ip_pqc()¶
Applies NIST-standardized post-quantum cryptography (CRYSTALS-Dilithium) to sign the generated IP, securing the design against future quantum threats.
from sc_neurocore.compiler.intelligence import protect_ip_pqc
pqc = protect_ip_pqc("sc_lif", {"v": "expr"})
print(f"Algorithm: {pqc.algorithm}")
print(f"Signature: {pqc.signature_hex}")
§74. Statistical Fault Injector — run_fault_campaign()¶
Runs a statistical fault injection campaign to measure the Soft Error Rate (SER) and Silent Data Corruption (SDC) rate of the compiled design.
from sc_neurocore.compiler.intelligence import run_fault_campaign
campaign = run_fault_campaign({"v": "expr"})
print(f"SDC Rate: {campaign.sdc_rate:.4f}")
§75. Static Timing Closer — verify_timing_closure()¶
Performs formal static timing analysis (STA) on the generated RTL to verify critical path delays before pushing to synthesis.
from sc_neurocore.compiler.intelligence import verify_timing_closure
timing = verify_timing_closure({"v": "expr"}, target_freq_mhz=250.0)
print(f"Timing met: {timing.timing_met}")
print(f"Slack: {timing.slack_ns} ns")
§76. Telemetry Ingestor — ingest_telemetry()¶
Closes the loop between the physical hardware and the compiler's digital twin by ingesting physical telemetry to detect drift and recalibrate the model.
from sc_neurocore.compiler.intelligence import ingest_telemetry
hw_data = [{"v": 1.0}, {"v": 1.1}]
twin_data = [{"v": 1.0}, {"v": 1.0}]
telemetry = ingest_telemetry(hw_data, twin_data, drift_threshold=0.05)
print(f"Healthy: {telemetry.healthy}")
Frontier Paradigm Co-Design (§77–§83)¶
§77. Omni-Paradigm Dispatcher — dispatch_omni_paradigm()¶
Partitions a monolithic SNN across multiple heterogeneous hardware paradigms simultaneously. Evaluates equation semantics to map stochastic variables to thermodynamic coprocessors, highly interconnected linear sums to optical crossbars, complex nonlinear integrations to CMOS, and entangled proxy states to quantum execution units.
from sc_neurocore.compiler.intelligence import dispatch_omni_paradigm
map_result = dispatch_omni_paradigm({"v": "-(v)/tau + I", "noise": "rand() * sigma"})
print(f"CMOS targets: {map_result.cmos_variables}")
print(f"Thermo targets: {map_result.thermodynamic_variables}")
§78. Reversible Logic Synthesizer — synthesize_reversible_logic()¶
Compiles state equations into Bennett-clocked reversible logic primitives (Toffoli and Fredkin gates), allowing deployment to adiabatic hardware architectures that operate at the Landauer limit of zero static energy dissipation.
from sc_neurocore.compiler.intelligence import synthesize_reversible_logic
reversible = synthesize_reversible_logic({"v": "v + I * R"})
print(f"Toffoli Gates: {reversible.toffoli_gates}")
print(f"Landauer Dissipation: {reversible.landauer_dissipation_kt} kT")
§79. Wetware MEA Mapper — map_wetware_mea()¶
Translates topological network connections and firing targets into exact physical recording and stimulation parameters for high-density Multi-Electrode Arrays (MEAs) interfacing with living biological brain organoids.
from sc_neurocore.compiler.intelligence import map_wetware_mea
mea_plan = map_wetware_mea(populations=1000, connectivity=0.8)
print(f"MEA density: {mea_plan.spatial_density}")
print(f"Stimulation: {mea_plan.stimulation_freq_hz} Hz")
§80. Morphological Auto-Synthesizer — synthesize_morphology()¶
Bypasses predefined ISAs entirely. Employs evolutionary simulation over equation interdependencies to co-design a completely new hardware routing topology physically structured to exactly match the SNN's dynamics (Zero-ISA Computing).
from sc_neurocore.compiler.intelligence import synthesize_morphology
morph = synthesize_morphology({"a": "b+c", "b": "a+c", "c": "a+b"})
print(f"Optimal Topology: {morph.topology}")
print(f"Required Bandwidth: {morph.bisection_bandwidth_gbps} Gbps")
§81. Cognitive Bound Enforcer — enforce_cognitive_bounds()¶
Analyzes equations for state-space criticality and automatically inserts hardware kill-switches into the generated RTL to prevent unconstrained dynamical divergence, ensuring safety for extreme-scale embodied architectures.
from sc_neurocore.compiler.intelligence import enforce_cognitive_bounds
safe = enforce_cognitive_bounds({"v": "v_old + dV"}, state_bounds={"v": (-65.0, 30.0)})
print(f"Switches inserted: {safe.switches_inserted}")
§82. Adiabatic Clock Generator — generate_adiabatic_clocks()¶
Generates the exact picosecond trapezoidal resonant clock timings (rise, hold, fall, sleep) required to drive energy-recovery adiabatic circuits without dissipating computational heat.
from sc_neurocore.compiler.intelligence import generate_adiabatic_clocks
clocks = generate_adiabatic_clocks(phases=4, freq_mhz=100.0)
print(f"Hold time: {clocks[0].hold_ps} ps")
§83. Holographic Interconnect Router — route_holographic_interconnects()¶
Calculates phase array matrices and Spatial Light Modulator (SLM) configurations to physically map ultra-high-fanout neural pathways via 3D free-space optical holographic projections, entirely sidestepping 2D planar wiring limits.
from sc_neurocore.compiler.intelligence import route_holographic_interconnects
router = route_holographic_interconnects(num_neurons=1000, connections=10_000_000)
print(f"Optical fanout: {router.optical_fanout_per_beam}")
Quick Reference Table¶
| § | Function | Category |
|---|---|---|
| 26 | generate_equivalence_sketch() |
Verification |
| 27 | partition_timescales() |
Optimisation |
| 28 | generate_provenance_chain() |
Traceability |
| 29 | generate_compliance_matrix() |
Certification |
| 30 | generate_energy_schedule() |
Power |
| 31 | lint_side_channels() |
Security |
| 32 | generate_drift_compensator() |
Reliability |
| 33 | plan_heterogeneous_dispatch() |
Deployment |
| 34 | recommend_target() |
Auto-Targeting |
| 35 | plan_partial_reconfiguration() |
FPGA |
| 36 | score_supply_chain_risk() |
Risk |
| 37 | generate_bittrue_kernel() |
Verification |
| 38 | classify_model_complexity() |
Analysis |
| 39 | CompilationCache |
Performance |
| 40 | estimate_thermal_envelope() |
Thermal |
| 41 | optimize_network_topology() |
Deployment |
| 42 | import_nir_graph() |
Import |
| 43 | verify_ode_stability() |
Verification |
| 44 | generate_power_intent() |
Power |
| 45 | estimate_carbon_footprint() |
ESG |
| 46 | insert_debug_probes() |
Debug |
| 47 | generate_memory_map() |
SoC |
| 48 | score_portability() |
Analysis |
| 49 | predict_reliability() |
Reliability |
| 50 | generate_fault_tree() |
Safety |
| 51 | generate_testbench() |
Verification |
| 52 | analyze_cdc() |
Verification |
| 53 | load_profiles_from_toml() |
Extensibility |
| 54 | plan_multi_die_floorplan() |
Deployment |
| 55 | check_regression() |
QA |
| 56 | check_license_compliance() |
Legal |
| 57 | generate_power_state_machine() |
Power |
| 58 | register_platform_hook() |
Extensibility |
| 59 | generate_compilation_report() |
Reporting |
| 60 | lint_hardware_trojans() |
Security |
| 61 | generate_sbom() |
Compliance |
| 62 | generate_hil_calibration() |
Calibration |
| 63 | generate_digital_twin() |
Deployment |
| 64 | map_ucie_protocol() |
Chiplet |
| 65 | schedule_seu_scrubbing() |
Space |
| 66 | obfuscate_ip() |
Security |
| 67 | embed_watermark() |
IP Protection |
| 68 | configure_approximation() |
Power |
| 69 | model_energy_harvest() |
Edge Compute |
| 70 | predict_aging() |
Reliability |
| 71 | generate_dvfs_controller() |
Power |
| 72 | explore_pareto() |
Optimisation |
| 73 | protect_ip_pqc() |
Security |
| 74 | run_fault_campaign() |
Verification |
| 75 | verify_timing_closure() |
Verification |
| 76 | ingest_telemetry() |
Digital Twin |
| 77 | dispatch_omni_paradigm() |
Deployment |
| 78 | synthesize_reversible_logic() |
Synthesis |
| 79 | map_wetware_mea() |
Biology |
| 80 | synthesize_morphology() |
Synthesis |
| 81 | enforce_cognitive_bounds() |
Safety |
| 82 | generate_adiabatic_clocks() |
Power |
| 83 | route_holographic_interconnects() |
Optical |
Further Reading¶
- Hardware Profiles Guide — all 191 profiles
- Frontier Platforms Guide — 39 platform classes
- Platform Extensibility Guide — TOML + hook + from_constraints
- Safety Certification Guide — DO-254, IEC 61508, ISO 26262
- Carbon & Sustainability Guide — ESG features
- Verification & Debug Guide — 10 V&V features
- Multi-Target Deployment Guide — heterogeneous deploy
- Static Analysis Guide — guard bits, overflow
- Deployment Guide — constraints, TCL, bitstream
- Co-Simulation Guide — Python↔Verilog
- Precision Modes Guide — Q-format modes