EvoSpikeNet function spec table
[!NOTE] For the latest implementation status, please refer to Functional Implementation Status (Remaining Functionality).
Creation date: 2026-04-01 Last actual measurement date: 2026-04-01 (local direct execution N=50 iterations) Purpose: Import and execute EvoSpikeNet-Core directly from the local Python environment and list the performance indicators of the unit test target functions based on actual measurements.
Actual measurement environment (2026-04-01) Execution mode: Import and execute EvoSpikeNet-Core directly from local venv. Docker/HTTP API is not used.
Host: Linux x86-64, CPU-only (no GPU), Python 3.12.3,/home/maoki/GitHub/.venvMeasurement tool:time.perf_counter_ns()+psutil, N=50 repeats Bench script:EvoSpikeNet-Core/benchmarks/feature_spec_local_bench.pyNote: MFCC extraction from raw waveform is not measured on this host due totorchaudioABI inconsistency. For the audio system, we actually measured the encoder itself with MFCC tensor input.
1. Definition of metrics
Prioritizes adoption of ``indicators that are measurable and effective for differentiation'' in comparison with other AI systems.
| Column heading | Unit | Explanation | Points of differentiation from other AIs |
|---|---|---|---|
| Cold Latency | ms | Initial call (cold start) time | Actual measurement of edge deployment and offline startup |
| Warm p50 / p90 / p99 | ms | Each percentile latency of stationary inference | Low tail latency shows real-time response advantage |
| Throughput | ops/s / FPS | Processing amount per unit time | Double-sided evaluation of batch processing and real-time stream |
| Peak RAM | MB | Peak memory usage during processing (RSS) | Basis for possibility of edge device operation |
| Disk size | MB | Storage occupancy of model weight + checkpoint | Comparison of deployment capacity (GPT-4 etc. are not disclosed and expected to exceed 300GB) |
| Spike firing rate | Hz | Average spike firing frequency | SNN specific index. Conventional AI (Dense Tensor) does not exist |
| Spike sparse density | % | Active neuron percentage (low = energy saving) | Direct indicator of computational efficiency/energy efficiency |
| Adaptive convergence steps | steps | Number of steps required to adapt to a new task | Quantification of continuous learning ability |
| Energy ratio | × (vs. conventional AI) | Relative power consumption compared to conventional CNN/Transformer | Quantification of energy saving advantage |
| Offline operation | ✓/✗ | Does it work without an internet connection? | Ability to operate independently without API dependence |
| Number of parallel nodes | Units | Maximum number of verified distributed nodes | Demonstration of distributed scalability |
| Test files | — | Corresponding unit test files (main ones) | Basis for test coverage |
Measurement tools:
time.perf_counter_ns()+psutil.Process.memory_info()+tracemallocStatistics: Each metric calculates mean/p50/p90/p99 with N≥30 iterations (Benchmark details: benchmarks/system_bench_plan_and_report.md) Actually measured: ☑ Target value/document value: ◎ Not measured (measurement required): — Test result legend: 🟢 All PASS / 🟡 Partial FAIL (X pass/Y fail) / 🔴 Error/All FAIL
2. Functional specifications table (by category)
2-1. Core SNN engine
| Function | Cold Latency (ms) | Warm p50 (ms) | Warm p90 (ms) | Peak RAM (MB) | Disk (MB) | Spike firing rate (Hz) | Spike sparse density (%) | Offline | Test results | Test file |
|---|---|---|---|---|---|---|---|---|---|---|
| LIF neuron model | ☑ 0.497 | ☑ 0.078 | ☑ 0.083 | ☑ 392.97 | < 1 | ◎ 20–100 | ◎ 5–15 % | ✓ | 🟢 1/0 | test_neuron_factory.py |
| Surrogate gradient (BPTT alternative) | ☑ 0.047 | ☑ 0.008 | ☑ 0.009 | ☑ 395.25 | < 1 | N/A | N/A | ✓ | 🟢 3/0 | test_surrogate.py |
| ChronoSpikeAttention | ☑ 0.054 | ☑ 0.012 | ☑ 0.013 | ☑ 395.25 | < 10 | ◎ 10–80 | ◎ 10–30 % | ✓ | 🟡 7/1 | test_attention.py, test_attention_shapes.py |
| Synapse basic operations | ☑ 0.225 | ☑ 0.036 | ☑ 0.038 | ☑ 396.43 | < 1 | N/A | N/A | ✓ | 🟢 11/0 | test_synapses.py |
| Izhikevich neuron / NMDA block | ☑ 0.376 | ☑ 0.120 | ☑ 0.127 | ☑ 394.21 | < 1 | ◎ 5–150 | ◎ 5–20 % | ✓ | 🟢 3/0 | test_nmda_block_and_izhikevich.py |
| Quantization utilities (int8/int16) | — | ◎ < 0.5 | ◎ < 2 | ◎ < 30 % reduction | ◎ 50 % reduction | N/A | N/A | ✓ | — | test_quantization_utils.py |
| Geometry/Structures | — | ◎ < 0.2 | ◎ < 1 | < 10 | < 1 | N/A | N/A | ✓ | 🟡 1/1 | test_structures.py, test_shapes_suite.py |
☑ 2026-04-01 Local direct measurement: LIF forward pass (CPU, n=1, dim=100) N=50: cold=0.497 ms, p50=0.078 ms, p90=0.083 ms. ChronoSpikeAttention was also under the same conditions, p50=0.012 ms, and the test passed 7 / failed 1.
2-2. Synaptic plasticity/learning
| Feature | Cold Latency (ms) | Warm p50 (ms) | Warm p90 (ms) | Peak RAM (MB) | Adaptive Convergence Step | Energy Ratio (×) | Offline | Test Results | Test File |
|---|---|---|---|---|---|---|---|---|---|
| STDP Basic | ☑ 0.204 | ☑ 0.063 | ☑ 0.066 | ☑ 397.14 | — | ◎ 0.6× | ✓ | 🟢 1/0 | test_stdp_modulation.py |
| Meta-STDP (self-adjusting type) | ☑ 0.101 | ☑ 0.047 | ☑ 0.048 | ☑ 421.22 | ◎ −75 % (vs basic STDP) | ◎ 0.4× | ✓ | 🟡 14/1 | test_meta_stdp.py |
| Neuromodulation STDP Gating | — | ◎ < 3 | ◎ < 15 | < 60 | — | ◎ 0.5× | ✓ | — | test_stdp_neuromodulation.py |
| Hierarchical Plasticity | ☑ 0.008 | ☑ 0.002 | ☑ 0.002 | ☑ 421.22 | — | N/A | ✓ | 🟢 5/0 | test_hierarchical_plasticity.py |
| Eligibility Traces | — | ◎ < 2 | ◎ < 8 | < 30 | — | N/A | ✓ | — | test_eligibility_traces.py |
| Plasticity Factory / Modulator | — | ◎ < 1 | ◎ < 3 | < 20 | — | N/A | ✓ | — | test_plasticity_factory.py, test_plasticity_modulator.py |
| Energy-dependent plasticity | ☑ 0.079 | ☑ 0.041 | ☑ 0.046 | ☑ 421.22 | — | ◎ 0.7× | ✓ | 🟢 18/0 | test_energy_plasticity.py |
| Distributed adaptive synchronization | — | ◎ < 10 | ◎ < 50 | < 100 | — | N/A | ✓ | — | test_adaptive_sync.py |
2-3. Neuromodulator system
| Function | Warm p50 (ms) | Warm p99 (ms) | Peak RAM (MB) | Spike firing rate change (%) | Offline | Test results | Test file |
|---|---|---|---|---|---|---|---|
| Dopamine / Reward Circuit | ◎ < 2 | — | < 30 | ◎ +20–50 | ✓ | 🟢 2/0 | test_reward_circuit.py, test_td_and_oxytocin.py |
| Acetylcholine (ACh) module | ◎ < 2 | — | < 20 | ◎ θ band +15 | ✓ | — | test_acetylcholine_module.py, test_adapter_ach_trigger.py |
| GABA / Excitation-inhibition balance | ◎ < 1 | — | < 20 | ◎ −30–60 | ✓ | 🟢 2/0 | test_gaba_and_network.py, test_gaba_tuning.py |
| Neuromodulatory gate integration | ◎ < 3 | — | < 50 | — | ✓ | — | test_neuromod_gate.py |
| Neuromodulation REST API ☑ | ☑ 4.1 | ☑ 28.8 | < 60 | — | ✓ | — | test_neuromod_rest_api.py |
| Emotion System | ◎ < 5 | — | < 40 | — | ✓ | 🟢 3/0 | test_emotion_system.py |
| Consciousness Circuit | ☑ 0.001 | — | ☑ 421.31 | — | ✓ | 🟡 34/5 | test_conscience_circuit.py |
| Rhythm synchronization (θ/γ) | ◎ < 2 | — | < 30 | ◎ γ band sync | ✓ | 🟡 3/2 | test_rhythm_sync.py |
| Neuromodulatory state (biomimetic API) ☑ | ☑ 4.1 | ☑ 28.8 | — | — | ✓ | — | REST /biomimetic/neuromod/state |
☑ Actual measurement (2026-04-01):
/biomimetic/neuromod/stateN=30: cold=74.2 ms, p50=4.1 ms, p90=5.3 ms, p99=28.8 ms
2-4. Storage system
| Feature | Cold Latency (ms) | Warm p50 (ms) | Throughput (ops/s) | Peak RAM (MB) | Disk (MB) | Offline | Test File |
|---|---|---|---|---|---|---|---|
| Hippocampal buffer (episodic memory) ☑ | ☑ 0.446 | ☑ 132.17 µs/op | ☑ 7566 | ☑ 423.87 | < 1 | ✓ | 🟡 8/2 |
| Hippocampal buffer (via API) ☑ | ☑ 43.2 | ☑ 5.2 | ☑ > 10⁴ | — | — | ✓ | — |
| Working memory | — | ◎ < 1 | ◎ > 10⁴ | < 50 | < 1 | ✓ | 🟢 3/0 |
| Long-term memory / hippocampal transfer | ☑ 0.036 | ☑ 0.007 | — | ☑ 424.65 | < 10 | ✓ | — |
| SNN memory extension | — | ◎ < 10 | — | < 200 | < 20 | ✓ | — |
| Sleep memory consolidation (STDP) | ◎ < 500 | ◎ < 50 | — | < 150 | < 5 | ✓ | 🟡 9/4 |
| Sleep–wake cycle ☑ | ☑ 4.3 | ☑ 4.4 | — | < 80 | < 1 | ✓ | 🟢 2/0 |
| Forgetting Controller | — | ◎ < 2 | — | < 30 | < 1 | ✓ | — |
| Tensor cache | ☑ 0.221 | ☑ 46.16 µs/op | ☑ 21662 | ☑ 424.57 | — | ✓ | 🟡 11/2 |
| Retention policy | — | ◎ < 1 | — | < 20 | — | ✓ | — |
| Memory Statistics API ☑ | ☑ 4.5 | ☑ 4.0 | — | — | — | ✓ | — |
☑ 2026-04-01 Local direct measurement: EpisodicMemory store cold=0.446 ms, p50=132.17 µs, throughput=7566 ops/s. TensorCache p50=46.16 µs, throughput=21662 ops/s. LongTermMemoryModule p50=0.007 ms.
2-5. Prefrontal cortex (PFC) ・Cognitive control
| Feature | Warm p50 (ms) | Warm p90 (ms) | Peak RAM (MB) | Number of parallel nodes | Offline | Test results | Test file |
|---|---|---|---|---|---|---|---|
| PFC Basic Routing ☑ | ☑ 0.027 | ☑ 0.045 | ☑ 428.70 | 1 | ✓ | 🟡 2/1 | test_pfc.py |
| Q-PFC (Quantum-inspired PFC) ☑ | ☑ 0.184 | ☑ 0.222 | ☑ 429.63 | 1 | ✓ | 🟡 21/2 | test_q_pfc_loop.py |
| Q-PFC Health API ☑ | ☑ 3.6 | ☑ 4.9 | — | — | ✓ | — | /q-pfc/api/v1/health |
| Q-PFC stats API ☑ | ☑ 2.7 | ☑ 592.8 * | — | — | ✓ | — | /q-pfc/api/v1/q-pfc/stats |
| Q-PFC adaptive control | ◎ < 20 | ◎ < 100 | < 200 | 1 | ✓ | — | test_q_pfc_adaptive_control.py |
| Distributed Q-PFC (28 nodes supported) | ◎ < 50 | ◎ < 200 | < 500 | ◎ 28 | ✓ | — | test_distributed_qpfc.py |
| Multi PFC cluster | ◎ < 30 | ◎ < 150 | < 400 | ◎ 10 | ✓ | — | test_multi_pfc_cluster.py, test_multipfc_cluster.py |
| Executive Control | ◎ < 10 | ◎ < 40 | < 120 | 1 | ✓ | — | test_executive_control.py |
| Intention Bias (Intention API) ☑ | ☑ 2.7 | ☑ 4.9 | < 80 | 1 | ✓ | — | /intention/current |
| PFC Decision Making (make_decision API) ☑ | ☑ 4.5 | ☑ 9.5 | < 150 | — | ✓ | — | /api/make_decision |
| Default mode network | ◎ < 10 | ◎ < 30 | < 100 | 1 | ✓ | — | test_dmn.py |
| Consensus decision making ☑ | ☑ 3.8 | ◎ < 80 | < 200 | ◎ 5+ | ✓ | — | /api/consensus/stats |
☑ Local direct measurement (2026-04-01): AdvancedPFCEngine forward N=50: cold=0.319 ms, p50=0.027 ms, p90=0.045 ms. QPFCAdaptiveController forward: cold=0.462 ms, p50=0.184 ms, p90=0.222 ms.
2-6. Sensory/perceptual input
| Features | Warm p50 (ms) | Warm p90 (ms) | Throughput | Peak RAM (MB) | Disk (MB) | Offline | Test results | Test files |
|---|---|---|---|---|---|---|---|---|
| Visual encoder ☑ | ☑ 0.105 | ☑ 0.111 | ◎ 15–30 FPS | ☑ 432.35 | < 50 | ✓ | 🟢 2/0 | test_vision.py |
| Audio encoder ☑ | ☑ 0.582 | ☑ 0.592 | — | ☑ 434.38 | < 20 | ✓ | 🟢 3/0 | test_audio.py |
| Audio → language conversion | ◎ 100–300 ms | — | — | < 300 | < 50 | ✓ | — | test_audio_to_language.py |
| EEG integration/translation | ◎ < 50 ms | — | ◎ > 100 ch | < 400 | < 5 | ✓ | — | test_eeg_integration.py, test_eeg_translator.py |
| EEG driver/device | ◎ < 20 ms | — | — | < 100 | < 1 | ✓ | — | test_eeg_drivers.py, test_eeg_drivers_device.py |
| Tactile → Language Conversion | ◎ < 100 ms | — | — | < 150 | < 5 | ✓ | — | test_tactile_to_language.py |
| LiDAR driver | ◎ < 10 ms | — | — | < 100 | < 1 | ✓ | — | test_lidar_driver.py |
| USB camera driver | ◎ < 33 ms | — | ◎ 30 FPS | < 50 | < 1 | ✓ | — | test_usb_camera_driver.py |
| Stereo Infrared / ONVIF | ◎ < 50 ms | — | — | < 150 | < 1 | ✓ | — | test_stereo_infrared_onvif_env.py |
| Sensor integration interface | ◎ < 5 ms | — | — | < 50 | — | ✓ | — | test_sensor_interface.py |
| Multimodal Fusion | ◎ 80–200 ms | — | — | < 500 | < 20 | ✓ | — | test_fusion.py |
☑ Local direct measurement (2026-04-01): SpikingVisionEncoder N=50: cold=0.425 ms, p50=0.105 ms, p90=0.111 ms. SpikingAudioEncoder is MFCC tensor input condition with cold=1.203 ms, p50=0.582 ms, p90=0.592 ms.
2-7. Spatial processing/3D generation
| Features | Warm p50 (ms) | Warm p90 (ms) | Throughput (FPS) | Peak RAM (MB) | Output size (MB) | Offline | Test file |
|---|---|---|---|---|---|---|---|
| Basic spatial processing | ☑ 0.009 | ☑ 0.009 | — | ☑ 439.33 | — | ✓ | test_spatial_processing.py |
| Spatial generation (high precision) | ◎ 10–50 ms | ◎ < 200 ms | ◎ 15–60 FPS | ◎ < 400 | < 5 MB/frame | ✓ | test_spatial_generation_high_precision.py |
| Spatial optimization | ◎ < 30 | ◎ < 120 | — | < 300 | — | ✓ | test_spatial_optim.py |
| Spatial model switching | ◎ < 5 | ◎ < 20 | — | < 100 | — | ✓ | test_spatial_models_toggle.py |
| Cortical topology construction | ☑ 18.246 | ☑ 18.525 | — | ☑ 444.83 | < 20 | ✓ | test_cortical_topology.py, test_cortical_topology_unit.py |
| Cortical Topology Export | ◎ < 100 | ◎ < 500 | — | < 800 | ◎ < 50 | ✓ | test_cortical_topology_export_save.py |
| 3D visualization | ◎ < 200 | ◎ < 1000 | — | < 1000 | — | ✓ | test_3d_visualization.py, test_cortical_topology_viz.py |
2-8. Language/Text Processing
| Feature | Warm p50 (ms) | Warm p90 (ms) | Throughput (tokens/s) | Peak RAM (MB) | Disk (MB) | Offline | Test results | Test file |
|---|---|---|---|---|---|---|---|---|
| Brain language architecture (overall) | ☑ 355.006 | — | — | ☑ 446.27 | < 100 | ✓ | 🟢 26/0 | test_brain_language.py, test_brain_language_comprehensive.py |
| Spike tokenizer | ☑ 0.006 | ☑ 0.006 | ☑ 10392223 | ☑ 444.83 | < 10 | ✓ | 🟢 1/0 | test_tokenizer.py, test_token_categories.py |
| Spike Transformer | ◎ < 10 | ◎ < 40 | ◎ > 1000 | < 400 | < 200 | ✓ | — | test_transformer.py |
| Language model (SNN-LM) | ◎ < 30 | ◎ < 100 | ◎ > 300 | < 600 | < 300 | ✓ | — | test_language_model.py |
| Text encoder | ◎ < 5 | ◎ < 20 | ◎ > 2000 | < 100 | < 20 | ✓ | — | test_text.py, test_encoding.py |
| Document parser (stream) | ◎ < 10 | ◎ < 50 | — | < 100 | — | ✓ | — | test_document_parsers.py, test_parsers_stream.py |
| Semantic chunking | ◎ < 20 | ◎ < 80 | — | < 150 | — | ✓ | — | test_semantic_chunking.py, test_chunking.py |
| Knowledge graph integration (RAG-SNN) | ◎ < 30 | ◎ < 100 | — | < 300 | — | ✓ | — | test_snn_rag.py |
2-9. RAG/Knowledge Search
| Feature | Warm p50 (ms) | Warm p90 (ms) | Index Size (GB) | Peak RAM (MB) | Offline | Test Results | Test File |
|---|---|---|---|---|---|---|---|
| RAG Query (API) ☑ | ☑ 40.3 | ☑ 55.7 | — | < 500 | ✓ (Local VS) | 🔴 errors | test_rag.py, /api/rag/query |
| SNN-RAG Hybrid | ◎ < 30 | ◎ < 120 | — | < 400 | ✓ | — | test_snn_rag.py |
| Milvus backend ☑ Running | ☑ 40.3 | ☑ 55.7 | — | < 200 | △ Running | 🔴 errors | test_rag_milvus.py |
| Elasticsearch backend ☑ Running | ☑ 3.2 | ☑ 5.5 | — | < 200 | △ Running | — | test_elasticsearch_client.py |
| Redis cache ☑ | ☑ 0.23 | ☑ 0.34 | — | < 50 | ✓ | — | /api Via Redis |
| RAG version API | ◎ < 20 | ◎ < 80 | — | < 100 | ✓ | — | test_rag_version_api.py, test_rag_client_versions.py |
| RAG WebSocket Progress | ◎ < 100 ms (RTT) | ◎ < 500 | — | < 100 | ✓ | — | test_rag_ws_progress.py |
| RAG multilingual (supports Japanese particles) | ◎ < 30 | ◎ < 100 | — | < 200 | ✓ | — | test_japanese_rag_particle_issue.py |
| RAG Debugging/Improvement | — | — | — | — | ✓ | — | test_rag_debug.py, test_rag_improvement.py |
☑ Actual measurement (2026-04-01): Milvus/RAG: rag/query cold=286.8 ms, p50=40.3 ms, p90=55.7 ms; ES cluster status=green; Redis SET/GET p50=0.23 ms
2-10. Evolution/Optimization Engine
| Feature | Cold Latency (ms) | Time per generation (ms) | Peak RAM (MB) | Disk (MB) | Generalization improvement rate | Offline | Test file |
|---|---|---|---|---|---|---|---|
| Genome initialization ☑ | ☑ 0.092 | — | ☑ 396.44 | < 1 | — | ✓ | 🟢 8/0 |
| Genome → Brain Forward ☑ | — | ☑ 1.943 | ☑ 446.66 | — | — | ✓ | — |
| Evolution Engine (Basic) | ◎ < 100 | ◎ < 500 | < 500 | < 10 | ◎ +15–25 % | ✓ | 🟡 29/2 |
| Evolution Status API ☑ | ☑ 4.3 | ☑ 5.1 | — | — | — | ✓ | — |
| Advanced Mutations | — | ☑ 0.059 | ☑ 446.66 | < 5 | — | ✓ | — |
| Coevolution | ◎ < 200 | ◎ < 1000 | < 800 | < 20 | ◎ +40 % (vs manual design) | ✓ | — |
| Maintain diversity | — | ◎ < 100 | < 100 | — | — | ✓ | — |
| Fitness evaluator | — | ◎ < 50 | < 100 | — | — | ✓ | — |
| Annealing optimization | ◎ < 500 | ◎ < 200 | < 100 | — | — | ✓ | — |
| Genome pool management | ◎ < 50 | — | < 200 | < 50 | — | ✓ | — |
| Distributed Evolution Engine | ◎ < 200 | ◎ < 2000 | < 1000 | < 50 | — | ✓ | — |
☑ 2026-04-01 Local direct measurement: EvoGenome initialization cold=0.092 ms, GenomeToBrainConverter p50=1.943 ms, MutationEngine p50=0.059 ms, GenomePool.evolve_generation(pop=10) p50=0.001 ms.
2-11. Quantum Inspire Function
| Features | Warm p50 (ms) | Warm p90 (ms) | Peak RAM (MB) | Metacognitive instability (ratio) | Offline | Test file |
|---|---|---|---|---|---|---|
| Quantum Layers | ◎ < 5 | ◎ < 20 | < 100 | — | ✓ | 🟢 3/0 |
| Quantum Interface | ◎ < 10 | ◎ < 50 | < 150 | — | ✓ | — |
| Quantum Enhancer | ◎ < 10 | ◎ < 40 | < 100 | — | ✓ | — |
| Quantum scene adjustment | ◎ < 20 | ◎ < 80 | < 200 | — | ✓ | — |
| Quantum Tuning | ◎ < 30 | ◎ < 100 | < 200 | — | ✓ | — |
| Quantum tomography | ☑ 92.657 | ☑ 735.723 | ☑ 450.88 | — | ✓ | test_quantum_tomography.py |
| Advanced quantum decision | ☑ 0.222 | ☑ 0.229 | ☑ 451.00 | ◎ +340 % (vs. conventional AI) | ✓ | test_advanced_quantum_decision.py |
| Q-PFC Profile | ◎ < 5 | ◎ < 20 | < 100 | — | ✓ | — |
| Q-PFC Advanced Extensions | ◎ < 30 | ◎ < 120 | < 250 | — | ✓ | — |
☑ Local direct measurement (2026-04-01): AdvancedQuantumDecisionMaker is measured with a real device under the binary selection condition of
num_options=2. cold=0.410 ms, p50=0.222 ms, p90=0.229 ms.
2-12. Distributed/Communication System
| Features | Dispatch reached ACK (ms) | E2E latency (ms) | Throughput (msg/s) | Number of parallel nodes | Offline | Test results | Test files |
|---|---|---|---|---|---|---|---|
| Distributed Brain Node ☑ | ☑ 2.6 | ◎ < 50 | ◎ > 1000 | ◎ 28 | LAN | — | test_distributed_brain_node.py, /api/distributed_brain/status |
| Zenoh PubSub (stats API) ☑ | ☑ 3.8 | ◎ < 10 | ◎ > 10⁴ | ◎ 28+ | LAN/WAN | 🟡 1/3 | test_zenoh_comm.py, /api/zenoh/stats |
| RAFT Consensus (stats API) ☑ | ☑ 3.8 | ◎ < 100 | — | ◎ 5–25 | LAN | — | test_raft_persistence.py, /api/consensus/stats |
| Node autonomous discovery ☑ | ☑ 2.7 | — | — | ◎ 28+ | LAN | — | /node-discovery/health |
| Dynamic load balancing API ☑ | ☑ 4.6 | — | — | ◎ 25 | LAN | — | test_dynamic_load_balancer.py, /api/loadbalancer/statistics |
| Distributed learning | — | ◎ < 500 | — | ◎ 10+ | LAN | — | test_distributed_training.py |
| Distributed evaluation | — | ◎ < 200 | — | ◎ 10+ | LAN | — | test_distributed_evaluation.py |
| Node communication delay tag | ◎ < 1 | — | — | — | LAN | — | test_communication_delay_tag.py, test_delay_tag_propagation.py |
| PTP time synchronization | ◎ < 1 | — | — | — | LAN | — | test_ptp_sync.py |
| Geographic Node Management ☑ | ☑ 2.5 | — | — | ◎ 28+ | — | — | /api/geo/nodes, test_geo_node_manager.py |
☑ Actual measurement (2026-04-01): Zenoh stats API N=30: cold=25.8 ms (first connection), p50=3.8 ms; RAFT stats p50=3.8 ms; LoadBalancer p50=4.6 ms; distributed_brain/status p50=2.6 ms * Zenoh library is placed in a Docker container. Direct import from host-side Python is not possible (zenoh package not installed). All functionality is available via API.
2-13. Security/Encryption
| Features | Warm p50 (ms) | Warm p99 (ms) | Encryption overhead | Key length/method | Offline | Test results | Test file |
|---|---|---|---|---|---|---|---|
| Spike encryption (XOR byte level) | ☑ 0.013 | ☑ 0.014 | ◎ < 5 % | XOR+biomimic enforcement opt-in | ✓ | 🟢 33/0 | test_spike_encryption.py |
| TLS Enforcement | ◎ < 5 | — | — | TLS 1.2/1.3 | — | — | test_tls_enforcement.py |
| mTLS mutual authentication | ◎ < 10 | — | — | Client certificate | — | — | test_mtls_auth.py |
| SSL Context | ◎ < 2 | — | — | — | ✓ | — | test_ssl.py, test_ssl_context.py |
| Safety filter | ◎ < 5 | — | — | — | ✓ | — | test_safety_filter.py |
| General Security | ◎ < 10 | — | — | OWASP Top10 compliant | ✓ | 🔴 errors | test_security.py |
| OPA Policy Authorization ☑ Running | ☑ 1.1 | ☑ < 2 | — | Rego Policy | ✓ | — | (OPA Docker: evospikenet-opa:8181) |
| File validator | ◎ < 1 | — | — | — | ✓ | — | test_file_validator.py |
☑ Local direct measurement (2026-04-01): AdvancedEncryptionEngine encrypt cold=0.904 ms, p50=0.013 ms, p90=0.014 ms. Spike encryption test passed 33 times / failed.
2-14. Energy/hardware optimization
| Feature | Warm p50 (ms) | Warm p90 (ms) | Energy Savings | Peak RAM (MB) | Offline | Test Results | Test File |
|---|---|---|---|---|---|---|---|
| Energy Tracking | ☑ 0.003 | ☑ 0.004 | — | ☑ 451.85 | ✓ | 🟡 22/1 | test_energy_tracker.py |
| Energy homeostasis | ◎ < 2 | — | ◎ 40 % | < 50 | ✓ | — | test_energy_homeostasis.py |
| Hardware Information API ☑ | ☑ 2.5 | ☑ 3.1 | ◎ 30–40 % | < 100 | ✓ | — | /api/hardware/info |
| Pipeline metrics API ☑ | ☑ 2.7 | ☑ 2.9 | — | — | ✓ | — | /api/pipeline/metrics |
| FPGA Safety | ◎ < 1 | — | — | < 10 | ✓ | — | test_fpga_safety.py |
| GPU operations | ◎ < 10 | — | — | GPU required | — | test_gpu_operations.py |
|
| CUDA Attention | ◎ < 5 | — | — | — | GPU required | — | test_cuda_attention.py |
| Model compression | ◎ < 100 | — | ◎ Disk 50–75 % reduction | < 200 | ✓ | — | test_model_compressor.py |
| Model quantization | ◎ < 50 | — | ◎ RAM −50 % | — | ✓ | — | test_quantization_utils.py |
| Batch optimization | ◎ < 5 | — | — | — | ✓ | — | test_batch_optimizer.py, test_batch_shaping.py |
☑ Direct local measurement (2026-04-01): CPU environment without GPU. EnergyTracker N=50: cold=0.016 ms, p50=0.003 ms, p90=0.004 ms. energy_tracker test passed 22 / failed.
2-15. Robustness/Automatic recovery
| Feature | MTTR / p50 (ms) | p90 (ms) | Success rate (%) | Peak RAM (MB) | Offline | Test results | Test file |
|---|---|---|---|---|---|---|---|
| Auto Recovery API ☑ | ☑ 3.0 | ◎ < 500 | ◎ > 99 | < 100 | ✓ | 🟡 32/1 | test_auto_recovery.py, /api/recovery/status |
| Snapshot list API ☑ | ☑ 2.9 | ◎ < 1000 | ◎ > 99.9 | < 200 | ✓ | — | /api/snapshot/list |
| Rollback | ◎ < 200 | — | ◎ 100 | < 100 | ✓ | — | test_rollback.py |
| Graceful Degradation | ◎ < 100 | — | ◎ > 95 | < 50 | ✓ | — | test_graceful_degradation.py |
| Safety Watchdog | ◎ < 10 | — | ◎ 100 | < 20 | ✓ | — | test_safety_watchdog_fix.py |
| Availability Monitor API ☑ | ☑ 2.5 | — | ◎ > 99.9 | < 30 | ✓ | — | /api/availability/status |
| Robustness test | — | — | ◎ > 98 | — | ✓ | — | test_robustness_tests.py |
| Error handling | ◎ < 1 | — | ◎ 100 | < 10 | ✓ | — | test_error_handling.py |
☑ Local direct measurement (2026-04-01): SnapshotManager.create_snapshot cold=1360.163 ms, p50=3.002 ms, p90=3.471 ms. AnomalyDetector initialization p50=0.001 ms. auto_recovery passed 32 / failed.
2-16. Monitoring/Auditing/Logging
| Features | Log write latency p50 (ms) | p90 (ms) | Storage growth rate | Offline | Test results | Test file |
|---|---|---|---|---|---|---|
| Audit Log stats API ☑ | ☑ 3.3 | ◎ < 1 write | ~1 MB/hour/node | ✓ | 🟡 17/2 | test_audit_log.py, /api/audit/stats |
| Availability status API ☑ | ☑ 2.5 | — | — | ✓ | — | /api/availability/status |
| Memory monitor | ◎ < 1 | — | — | ✓ | — | test_memory_monitor.py |
| Centralized logger | ◎ < 2 | — | — | ✓ | — | test_centralized_logger.py |
| Log analysis | ◎ < 50 | — | — | ✓ | — | test_log_analysis.py |
| Metrics API ☑ | ☑ 130.1 | — | — | ✓ | — | /metrics (Prometheus format) |
| Evolution Dashboard | ◎ < 100 | — | — | ✓ | — | test_evolution_dashboard.py |
| Metadata handler | ◎ < 2 | — | — | ✓ | — | test_metadata_handler.py |
☑ Local direct measurement (2026-04-01): AuditLogManager.log p50=1.10 µs, p90=1.14 µs, throughput=908265 writes/s. Availability monitor initialization p50=0.001 ms.
2-17. SDK/API/External collaboration
| Feature | Warm p50 (ms) | Warm p90 (ms) | Offline | Test results | Test file |
|---|---|---|---|---|---|
| Python SDK initialization ☑ | ☑ 0.020 | ☑ 0.024 | ✓ | 🟡 48/4 | test_sdk.py, test_sdk_validation.py |
| REST API /api/health ☑ | ☑ 2.0 | ☑ 2.2 | ✓ | — | /api/health |
| REST API latency_check ☑ | ☑ 3.8 | ☑ 4.6 | ✓ | 🔴 errors | test_api_endpoints.py, /api/latency_check |
| WebSocket Asynchronous Pipeline | ◎ < 50 | ◎ < 200 | ✓ | — | test_async_pipeline.py |
| SDK backup | ◎ < 100 | ◎ < 500 | ✓ | — | test_sdk_backup.py |
| SDK sensor cooperation | ◎ < 10 | ◎ < 50 | ✓ | — | test_sdk_sensors.py |
| SDK RAG cooperation | ◎ < 20 | ◎ < 80 | ✓ | — | test_sdk_rag.py |
| SDK Jupyter integration | ◎ < 200 | ◎ < 1000 | ✓ | — | test_sdk_jupyter.py |
| Universal Integration Adapter | ◎ < 30 | ◎ < 100 | ✓ | — | test_universal_integration.py |
| Frontend UI | ◎ < 200 | ◎ < 1000 | ✓ | — | test_frontend.py, test_frontend_ui.py |
☑ Local direct measurement (2026-04-01): SDK init cold=0.111 ms, p50=19.74 µs, p90=24.00 µs. HTTP API latency is not subject to this update as it is a local direct execution bench.
3. Comparison summary with other AIs
Detailed comparison: benchmarks/evo_vs_traditional_report.md Detailed comparison (5 perspectives): benchmarks/evo_vs_traditional_detailed.md
| Comparison metrics | EvoSpikeNet | ChatGPT (gpt-5.4) | Claude (claude-sonnet-4-6) |
|---|---|---|---|
| Model size (Disk) | Several MB–several hundred MB by module | Private (estimated at 300GB+) | Private (estimated at 200GB+) |
| Memory Usage | ☑ Minimum ≈ 2.21 MB ~ Several GB | Private (via API) | Private (via API) |
| Cold start | ☑ Genome → Brain ≈ 5.73 ms | Hundreds of ms to seconds (API round trip) | Hundreds of ms to seconds (API round trip) |
| Inference Latency p50 | ☑ 0.65 ms (forward, CPU) / ☑ 2.0 ms (REST API) | 300–1000 ms (API) | 300–800 ms (API) |
| REST API p90 | ☑ 2.2 ms (health) / 55.7 ms (RAG) | Private | Private |
| Offline operation | ✓ (Includes LAN-only function) | ✗ (Internet required) | ✗ (Internet required) |
| Spike Dense Density | ◎ Activated only 5–30% | N/A (Dense Tensor) | N/A (Dense Tensor) |
| Continuous learning | ◎ Adaptation time −75 % (Meta-STDP) | ✗ (Fine-tune separately) | ✗ (Fine-tune separately) |
| Distributed nodes | ◎ Supports 28 nodes / Zenoh p50 ☑ 3.8 ms | N/A | N/A |
| Metacognitive flexibility | ◎ +340 % (Q-PFC, human judgment) | None | None |
| Energy efficiency | ◎ 30–70% reduction compared to conventional CNN (CPU operation confirmed) | Private | Private |
| Context length | N/A (neuronal memory) | 1M tokens | 1M tokens |
| Number of unit tests | ☑ 332 files | Private | Private |
| Number of running Docker services | ☑ 9 services | — | — |
| API endpoint count | ☑ 187 endpoints | Private | Private |
4. Measurement items to add (next action)
The following indicators are currently only "target values", but adding actual measurement benches will increase differentiation strength.
| Priorities | Measurements | Recommended bench scripts | Goals |
|---|---|---|---|
| High | Zenoh inter-node RTT/throughput | benchmarks/dispatch_bench.py |
< 2 ms, > 10⁴ msg/s |
| High | Spatial generation latency (by resolution) | benchmarks/spatial_gen_bench.py |
20 ms @ 640×480 |
| High | Object Recognition mAP / FPS | benchmarks/object_recog_bench.py |
mAP > 0.7, ≥ 20 FPS |
| Medium | E2E pipeline (perception → action) | benchmarks/e2e_bench.py |
< 200 ms |
| Medium | Memory retention rate after sleep consolidation | tests/unit/test_sleep_consolidation_stdp.py |
> 90 % |
| Medium | Distributed 28-node throughput | benchmarks/dispatch_bench.py --nodes 28 |
Linear scale ≥ 0.8 |
| Low | GPU vs CPU energy ratio | Custom script +nvml |
−60% for GPU |
| Low | API latency comparison (OpenAI/Anthropic) | benchmarks/api_bench.py |
EvoSpikeNet p50 < API p50 |
5. future_apps — Robotics/BMI application response speed
Shows the real-time response performance target value of each future_apps that implements EvoSpikeNet-Core.
Each app is based on the KPIs listed in implementation_plan.md and is continuously verified using the corresponding test script.
Legend: ◎ = KPI target value described in implementation_plan.md, — = undefined
5-1. Humanoid robot control (humanoid)
Role: EvoSpikeNet Fleet nodes with distributed brains at the edge.
Orchestrator: cooperative_edge_robotics_system (REST + Zenoh)
| Features / Loops | Response Time Goal | Update Rate | Success Rate | Notes | Test File |
|---|---|---|---|---|---|
| Sensor → Brain round trip delay | ◎ < 50 ms | — | — | Local network | test_sensor_brain_loop.py |
| per-node 3D mapping processing | ◎ 33–100 ms/frame | ◎ 10–30 Hz | — | occipital_3d_mapper | test_biped_simulation.py |
| Map fusion update | ◎ 200 ms/update | ◎ 5 Hz | — | Multiple node integration | test_full_loop.py |
| Orchestrator connection establishment | ◎ < 10,000 ms | — | ◎ 100 % | First time after startup | test_orchestrator_client.py |
| Lost connection → Standalone migration | ◎ < 5,000 ms | — | ◎ 100 % | Failover | test_brain_integration.py |
| Heartbeat p99 latency ☑ | ☑ 3.7 ms | ◎ 5 Hz | ◎ 100 % | Actual measurement of 30 units fleet | tests/load/fleet_load_test.py |
| Node registration p95 latency | ◎ ≤ 500 ms | — | ◎ 100 % | 30 devices registered at the same time | tests/load/fleet_load_test.py |
| Motion control loop | — | ◎ > 30 Hz | — | 120 Motor control | test_motion_manager.py |
| GPU inference pipeline (CUDA/CPU) | — | ◎ > 10 Hz | — | Asynchronous queue method | test_pytorch_integration.py |
| Physical simulation (PyBullet) | — | ◎ > 60 Hz | — | For safety verification | test_pybullet_simulation.py |
☑
tests/load/fleet_load_test.pyexecution result: nodes_completed=30/30, reg 100%, hb p99=3.7 ms
5-2. Cooperative Edge Robotics Orchestrator (cooperative_edge_robotics_system)
Role: A server that controls role assignment, mission planning, and federated learning for a multi-robot fleet (FastAPI port 8025).
| Features/Services | Response Time Goals | Throughput Goals | Success Rate/Accuracy | Notes | Test Files |
|---|---|---|---|---|---|
| Mission planning latency (10 robots) | ◎ < 1,000 ms | — | — | Task decomposition/dependency graph generation | test_mission_planner.py |
| Node alive monitoring response | ◎ < 500 ms | — | — | Heartbeat loss detection | test_node_registry.py |
| Task scheduling | ◎ < 100 ms | ◎ ≥ 100 tasks/s | — | Priority queue processing | test_task_scheduler.py |
| Dynamic role assignment | — | — | — | Capability matching | test_fleet_diagnostics.py |
| Federated learning Convergence generation number | — | — | ◎ ≤ 50 rounds | With differential privacy | test_pytorch_integration.py |
| Fleet resource utilization rate | — | — | ◎ ≥ 80 % | All nodes aggregation | test_fleet_diagnostics.py |
| Failure Prediction (FleetDiagnostics) | — | — | ◎ ≥ 85 % | Sensor/Actuator Diagnostics | test_fleet_diagnostics.py |
| E2E Fleet Pipeline | — | — | — | Plan → Schedule → Zenoh Delivery | (integration) test_30_humanoid_fleet.py |
| API routes (REST) | ◎ < 50 ms | — | — | FastAPI all endpoints | test_api_routes.py |
| Zenoh message delivery RTT | ◎ < 5 ms | ◎ > 10⁴ msg/s | — | Real-time communication between robots | (Refer to Zenoh bench) |
5-3. Autonomous robot control (autonomous_robotics_control)
Role: SNN-driven perception-planning-execution cycle (grasping, transport, collision avoidance).
| Features/Components | Response Time Objectives | Update Rate/Accuracy | Notes | Test Files |
|---|---|---|---|---|
| Sensor fusion (LiDAR/Camera/IMU) | ◎ 20–50 ms | — | BEV generation/point cloud processing | test_performance.py |
| Collision avoidance response | ◎ < 100 ms | — | Real-time safety control | test_system.py |
| Robot controller (joint control) | ◎ < 10 ms | ◎ > 100 Hz | Inverse kinematics/actuator transmission | test_unit.py |
| Object recognition (SNN inference) | — | ◎ ≥ 95 % accuracy | Grasp point estimation ≥ 85 % | test_full_pipeline.py |
| Motion planning (RRT*/MoveIt compatible) | ◎ < 200 ms | — | Trajectory optimization | test_full_pipeline.py |
| DNA motion encoder | — | — | Evolutionary motion optimization | test_unit.py |
| Distributed SNN inference (task distribution) | ◎ < 50 ms | — | Multi-node synchronization delay guarantee | test_distributed_coordinator_only.py |
| GPU inference bench | — | — | CUDA/CPU comparison measurement | test_gpu_bench.py |
| LLM workflow integration | ◎ < 1,000 ms | — | Higher-level task understanding/instruction interpretation | test_llm_workflow.py |
| Task success rate (gripping/transferring) | — | ◎ ≥ 90 % | Continuous operation ≥ 8 hours | test_integration.py |
5-4. Location-aware team robotics (location_aware_team_robotics)
Role: Precise position estimation and formation control using UWB/LiDAR/IMU sensor fusion EKF.
| Features/Components | Response Time Objectives | Update Rate/Accuracy | Notes | Test Files |
|---|---|---|---|---|
| Self-localization (EKF) | ◎ 20 ms | ◎ ≥ 50 Hz | RMSE ≤ 5 cm | test_full_pipeline.py |
| Path planning (A/RRT, 10 m path) | ◎ < 100 ms | — | Dynamic obstacle avoidance | test_full_pipeline.py |
| Task assignment (10 robots) | ◎ < 500 ms | — | Auction method | test_full_pipeline.py |
| Formation control loop (ORCA) | ◎ 100 ms | — | Formation error RMS ≤ 10 cm | test_robotics_extended.py |
| Coordination of the entire team (TeamCoordinator) | ◎ 500 ms/update | — | Consider role reassignment | test_api_routes.py |
| Collision avoidance success rate | — | ◎ 99.9 % | ORCA algorithm | test_robotics_extended.py |
| GPU bench | — | — | Inference acceleration measurement | test_gpu_bench.py |
| LLM workflow integration | ◎ < 1,000 ms | — | Higher order instruction interpretation | test_llm_workflow.py |
5-5. Brain Machine Interface (BMI) (brain_machine_interface)
Role: EEG real-time processing, motor intention decoding, neurofeedback control, clinical rehabilitation management.
| Features/Services | Response Time Objectives | Sampling/Accuracy | Notes | Test Files |
|---|---|---|---|---|
| EEG signal stream processing | ◎ < 10 ms | ◎ 1000 Hz / 256 ch | Bandpass filter/ICA/CSP | test_signal_interface.py |
| Motor intention decoding | ◎ < 50 ms | ◎ ≥ 95 % accuracy | SNN inference/left-hand discrimination | test_bmi_services.py |
| Add neurofeedback | ◎ < 100 ms | — | Visual/haptic feedback | test_bmi_services.py |
| Device communication (OpenBCI/Emotiv, etc.) | ◎ < 5 ms | — | Multi-device unified IF | test_signal_interface.py |
| Safety monitoring (emergency stop) | ◎ < 10 ms | ◎ 100 % detection rate | Stimulus intensity/biological threshold monitoring | test_bmi_services.py |
| Distributed SNN inference (multiple nodes) | ◎ < 50 ms | — | Load balancing/redundancy | test_bmi_extensions.py |
| BMI session management | ◎ < 200 ms | — | Start/end/state transition | test_bmi_services.py |
| Rehabilitation program execution | — | — | Treatment planning/progress tracking | test_bridge_program_uplift_api.py |
| Operational Uplift API | ◎ < 200 ms | — | REST API for Clinicians | test_operational_uplift_api.py |
5-6. future_apps cross-cutting summary — response time comparison
| Apps | Most Important Control Loops | Target Latency | Actual Values | Offline | Fleet Size |
|---|---|---|---|---|---|
| humanoid | Sensor → Brain → Actuator | < 50 ms | ☑ HB p99 = 3.7 ms | LAN | 30 units |
| cooperative_edge | Mission planning (10 robots) | < 1,000 ms | — | LAN | 30+ |
| autonomous_robotics | Joint control loop | < 10 ms | — | LAN/Standalone | Single |
| location_aware_team | EKF location estimation | 20 ms | — | LAN | 10+ |
| brain_machine_interface | EEG processing→feedback | < 100 ms | — | — | — |
| EvoSpikeNet-Core (reference) | genome→brain forward | < 1 ms | ☑ 0.65 ms | ✓ | — |
This table is a combination of the measured values under benchmarks/ and the design values under Docs/. *
☑ = Actual value (bench_report.md / fleet_load_test), ◎ = Document KPI target value, — = Not measured*