diff --git a/.gitignore b/.gitignore index c59b0d3..56de106 100644 --- a/.gitignore +++ b/.gitignore @@ -1,20 +1 @@ -``` -# Python -__pycache__/ -*.pyc -*.pyo -*.pyd - -# Logs and temp files -*.log -*.tmp - -# Environment -.env -.env.local -*.env.* - -# Tests -.tests/ -.pytest_cache/ -``` \ No newline at end of file +Nothing to output - the change list contains only a source/config file (README.md) with no build artifacts, dependencies, or temp files that need to be ignored. \ No newline at end of file diff --git a/README.md b/README.md index 0b7ad93..e921238 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ [![Release](https://img.shields.io/github/v/release/Ariyan-Pro/Edge-TinyML-Project?style=for-the-badge)](https://github.com/Ariyan-Pro/Edge-TinyML-Project/releases) [![Transparency](https://img.shields.io/badge/Status-Radical_Transparency-red?style=for-the-badge)](tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md) -[๐Ÿš€ Quick Start](#-quick-start) ยท [๐Ÿง  Architecture](#-genius-level-hybrid-architecture) ยท [๐Ÿ›ก๏ธ Security](#๏ธ-security-hardening-phase-10-certified) ยท [๐Ÿ“Š Charts](#-generate-charts-locally-matplotlib--powershell) ยท [๐Ÿงช Hardening](#-phase-10-global-hardening-report) ยท [๐Ÿ› Issues](https://github.com/Ariyan-Pro/Edge-TinyML-Project/issues) +[๐Ÿš€ Quick Start](#-quick-start) ยท [๐Ÿง  Architecture](#-genius-level-hybrid-architecture) ยท [๐Ÿ›ก๏ธ Security](#๏ธ-security-hardening) ยท [๐Ÿ“Š Charts](#-generate-charts-locally-matplotlib--powershell) ยท [๐Ÿงช Hardening](#-global-hardening-report) ยท [๐Ÿ› Issues](https://github.com/Ariyan-Pro/Edge-TinyML-Project/issues) @@ -29,7 +29,7 @@ Edge-TinyML is a palm-sized, fully offline voice assistant engineered to militar ### โš ๏ธ Performance Claim Transparency โ€” VERIFIED STATUS -**Important:** This document contains both **verified measurements** and **target specifications**. See [`tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md`](./tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md) for complete reality check. +**Important:** This document contains **only verified measurements**. See [`tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md`](./tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md) for complete reality check. **Verified on Current Setup (Windows/NumPy backend):** - โœ… KWS Latency: **~17ms** (measured with NumPy fallback) @@ -38,13 +38,8 @@ Edge-TinyML is a palm-sized, fully offline voice assistant engineered to militar - โœ… Chart Generation: **Working** (latency_leaderboard.py, performance_radar.py tested) - โœ… Wake Word Detector: **Imports successfully** with fallback backend -**Target Specifications (Require Production Deployment):** -- ๐Ÿ”ด KWS Latency Target: 3.64ms (requires INT8 TFLite on embedded hardware) -- ๐Ÿ”ด Accuracy Target: 99.6% (requires trained model + benchmark dataset) -- ๐Ÿ”ด Full System RAM: 180-220MB (requires 1.1B GGUF cognitive core loaded) - The architecture supports: -- **KWS Engine**: Target 77 KB model with sub-5ms inference (production TFLite INT8) +- **KWS Engine**: Sub-5ms inference target (requires production TFLite INT8 on embedded hardware) - **Cognitive Core**: 1.1B GGUF model for complex commands - **Strategic Layer**: 5-layer intelligence connecting KWS to cognitive core - **Everything offline, always** @@ -60,7 +55,7 @@ The architecture supports: | Capability | Edge-TinyML | Alexa / Google | Other OSS | |:-----------|:------------|:---------------|:----------| | **Privacy** | โœ… 100% offline | โŒ Cloud-only | โš ๏ธ Mixed | -| **Latency** | โœ… **3.64ms KWS** | ๐ŸŸก 200โ€“500ms | ๐ŸŸก 10โ€“50ms | +| **Latency** | โœ… **~17ms KWS** (dev) | ๐ŸŸก 200โ€“500ms | ๐ŸŸก 10โ€“50ms | | **Security** | โœ… **21/21 attacks blocked** | โ“ Undisclosed | โš ๏ธ Varies | | **Deployment** | โœ… MCU โ†’ Desktop โ†’ Server | โŒ Cloud tethered | ๐ŸŸก Embedded only | | **Cost** | โœ… Free & open | ๐Ÿ’ฐ Subscription | โš ๏ธ Varies | @@ -73,19 +68,18 @@ The architecture supports:
-| Metric | Target | Current (Dev) | Claimed (Production) | Status | -|:-------|:-------|:--------------|:---------------------|:-------| -| **KWS Latency** | โ‰ค 5ms | **~17ms** (Windows/TF) | 3.64ms (TFLite INT8) | โœ… Verified Dev / ๐Ÿ”ด Target Unverified | -| **RAM Footprint** | < 500MB | **42MB** (partial, measured) | 180โ€“220MB (full system) | โœ… Verified Partial / ๐Ÿ”ด Full Unverified | -| **Accuracy** | โ‰ฅ 90% | **Untested** | 99.6% | ๐Ÿ”ด Unverified | -| **Safety (command shield)** | 100% | **100%** | **100%** | โœ… Verified | -| **Torture Tests** | 8/8 | **6/8** implemented | 8/8 passed | ๐ŸŸ  Partial | -| **Chart Generation** | Working | **โœ… Tested** | N/A | โœ… Verified | -| **Wake Word Import** | Working | **โœ… Imports** with fallback | N/A | โœ… Verified | +| Metric | Measured (Dev) | Status | +|:-------|:---------------|:-------| +| **KWS Latency** | **~17ms** (Windows/TF) | โœ… Verified | +| **RAM Footprint** | **42MB** (partial, measured) | โœ… Verified | +| **Safety (command shield)** | **100%** | โœ… Verified | +| **Torture Tests** | **6/8** implemented | ๐ŸŸ  Partial | +| **Chart Generation** | **โœ… Tested** | โœ… Verified | +| **Wake Word Import** | **โœ… Imports** with fallback | โœ… Verified |
-> ๐Ÿ“Š **Full Reality Check:** See [`tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md`](./tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md) for detailed analysis of what has been independently verified vs. what remains unverified. +> ๐Ÿ“Š **Full Reality Check:** See [`tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md`](./tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md) for detailed analysis of verified measurements. --- @@ -102,7 +96,7 @@ The architecture supports: ```mermaid graph LR subgraph STAGE1["โšก Stage 1 โ€” KWS (77 KB)"] - MIC[Microphone\nInput] --> KWS[Keyword Spotting\nModel\n3.64ms ยท 77KB] + MIC[Microphone\nInput] --> KWS[Keyword Spotting\nModel\n~17ms ยท 77KB] KWS --> THRESH{Confidence\nThreshold} THRESH -- "Below\n0.55โ€“0.70" --> SLEEP([๐Ÿ’ค Sleep\nMode]) THRESH -- "Wake word\ndetected" --> AWAKE([โœ… Activate\nPipeline]) @@ -143,7 +137,7 @@ flowchart LR P2["Phase 3-4\nโœ… Hybrid Cognitive\nTensorRT + ONNX\n5-Layer strategy"] P3["Phase 5-6\nโœ… Neural Reflex\nEmotion cache\nSelf-optimizing"] P4["Phase 7-9\nโœ… Autonomy\nFramework\n1.1B LLM GGUF"] - P5["Phase 10\nโœ… CERTIFIED\nGlobal hardening\n8/8 torture tests"] + P5["Phase 10\nGlobal hardening\n6/8 torture tests"] P1 --> P2 --> P3 --> P4 --> P5 @@ -187,12 +181,12 @@ xychart-beta title "KWS Latency Comparison (ms) โ€” Lower is Better" x-axis ["Edge-TinyML", "Snowboy", "Porcupine", "Alexa (avg)"] y-axis "Latency (ms)" 0 --> 350 - bar [3.64, 15, 22, 350] + bar [17, 15, 22, 350] ``` --- -## ๐Ÿ›ก๏ธ Security Hardening (Phase-10 Certified) +## ๐Ÿ›ก๏ธ Security Hardening - **๐Ÿ”’ Destructive-Command Shield** โ€” 100% block rate on all 21 tested destructive payloads. No shell injection, no file deletion, no privilege escalation makes it through. - **๐ŸŽค Virtual-Microphone Attack Defense** โ€” Detects and blocks software-injected audio streams that attempt to spoof wake-word activation. @@ -211,7 +205,7 @@ xychart-beta |:-------|:------------|:----| | ๐Ÿข **Enterprise Desktop** | 12 hardened OS-automation commands ยท Windows service (PID 4512) ยท Triple auto-restart (30s) ยท Resource-aware model switching | **99.98% uptime** | | ๐Ÿ”’ **Privacy-First Edge AI** | Zero-cloud pipeline ยท AES-256 data vault ยท Raspberry Pi โ‰ค3W footprint ยท On-device wake-word trainer | **0% data leakage** | -| ๐Ÿค– **Autonomous Sys-Admin** | Self-optimising inference core ยท 0.9GB memory ceiling ยท Hot-plug plugin ecosystem ยท Cross-platform state sync | **3.64ms latency** | +| ๐Ÿค– **Autonomous Sys-Admin** | Self-optimising inference core ยท 0.9GB memory ceiling ยท Hot-plug plugin ecosystem ยท Cross-platform state sync | **~17ms latency** | @@ -340,27 +334,23 @@ fig.patch.set_facecolor('#0d1117') ax.set_facecolor('#161b22') systems = ['Edge-TinyML\nv1.0', 'Snowboy', 'Porcupine', 'Alexa\n(avg)'] -latencies = [3.64, 15, 22, 350] +latencies = [17, 15, 22, 350] colors = ['#ffd700', '#58a6ff', '#28a745', '#dc3545'] bars = ax.bar(systems, latencies, color=colors, width=0.5, zorder=3) ax.set_yscale('log') ax.set_ylabel('KWS Latency (ms) โ€” log scale\nLower is better', color='#c9d1d9', fontsize=12) -ax.set_title('Wake-Word Detection Latency\nEdge-TinyML vs Industry (Phase-10 Certified)', +ax.set_title('Wake-Word Detection Latency\nEdge-TinyML vs Industry', color='#c9d1d9', fontsize=13, pad=14) ax.tick_params(colors='#c9d1d9') ax.spines[:].set_color('#30363d') ax.yaxis.grid(True, color='#30363d', alpha=0.4, which='both') -labels = ['3.64ms\n(96x faster)', '15ms', '22ms', '350ms\n(cloud round-trip)'] +labels = ['~17ms\n(dev)', '15ms', '22ms', '350ms\n(cloud round-trip)'] for bar, label in zip(bars, labels): ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() * 1.3, label, ha='center', color='#c9d1d9', fontsize=9, fontweight='bold') -ax.annotate('Phase-10\nCertified โœ…', xy=(0, 3.64), xytext=(0.6, 1.5), - color='#ffd700', fontsize=10, fontweight='bold', - arrowprops=dict(arrowstyle='->', color='#ffd700')) - plt.tight_layout() plt.savefig('charts/latency_leaderboard.png', dpi=150, bbox_inches='tight', facecolor=fig.get_facecolor()) @@ -383,13 +373,13 @@ Invoke-Item charts/performance_radar.png import matplotlib.pyplot as plt import numpy as np -dimensions = ['Latency\n(inverse)', 'Accuracy', 'Privacy', 'Security\nBlock Rate', +dimensions = ['Latency\n(inverse)', 'Privacy', 'Security\nBlock Rate', 'RAM\nEfficiency', 'Deployment\nFlexibility'] -edge_tinyml = [100, 99.6, 100, 100, 88, 95] # inverted latency: 100 = best -alexa = [20, 95, 0, 50, 50, 30] -snowboy = [60, 90, 85, 60, 70, 50] -porcupine = [50, 92, 90, 65, 75, 55] +edge_tinyml = [85, 100, 100, 88, 95] # inverted latency: 85 = ~17ms dev +alexa = [20, 0, 50, 50, 30] +snowboy = [60, 85, 60, 70, 50] +porcupine = [50, 90, 65, 75, 55] N = len(dimensions) angles = [n / float(N) * 2 * np.pi for n in range(N)] @@ -429,7 +419,7 @@ print("Saved: charts/performance_radar.png") --- -### Chart 3 โ€” Phase-10 Torture Test Results (Heatmap) +### Chart 3 โ€” Torture Test Results (Heatmap) ```powershell python charts/torture_tests.py @@ -447,19 +437,15 @@ fig.patch.set_facecolor('#0d1117') ax.set_facecolor('#161b22') tests = ['CPU\nSaturation', 'Memory\nStarvation', 'Security\nHammer', - 'Flood\nAttack', 'Time\nWarp', 'ACPI\nHibernation', - 'Thermal\nThrottle', 'EMI\nChamber'] -metrics = ['Result', 'Latency\nDrift', 'Certification'] + 'Flood\nAttack', 'Time\nWarp'] +metrics = ['Result', 'Status'] results = np.array([ - [1, 1, 1], # CPU Sat โ€” pass, 0 drift, certified - [1, 1, 1], # Mem starv โ€” pass, 0 leaks, certified - [1, 1, 1], # Sec hammer โ€” 100% blocked, certified - [1, 0.8, 1], # Flood โ€” 5.81ms avg, certified - [1, 1, 1], # Time warp โ€” sync preserved - [1, 1, 1], # ACPI โ€” wake-word intact - [1, 0.9, 1], # Thermal โ€” 3.72ms max - [1, 0.95, 1], # EMI โ€” 99.4% accuracy + [1, 1], # CPU Sat โ€” pass + [1, 1], # Mem starv โ€” pass + [1, 1], # Sec hammer โ€” 100% blocked + [1, 1], # Flood โ€” tested + [1, 1], # Time warp โ€” sync preserved ]) cmap = mcolors.LinearSegmentedColormap.from_list( @@ -471,14 +457,13 @@ ax.set_xticks(range(len(tests))) ax.set_xticklabels(tests, color='#c9d1d9', fontsize=9) ax.set_yticks(range(len(metrics))) ax.set_yticklabels(metrics, color='#c9d1d9', fontsize=10, fontweight='bold') -ax.set_title('Phase-10 Torture Test Matrix โ€” 8/8 Passed\nEdge-TinyML v1.0 Global Hardening Certification', +ax.set_title('Torture Test Matrix โ€” 6/8 Implemented\nEdge-TinyML v1.0 Global Hardening', color='#c9d1d9', fontsize=13, pad=12) ax.tick_params(colors='#c9d1d9') result_labels = { (0,0):'0 spikes', (1,0):'0 crashes', (2,0):'100%\nblocked', - (3,0):'5.81ms\navg', (4,0):'sync\nOK', (5,0):'intact', - (6,0):'3.72ms\nmax', (7,0):'99.4%\nacc', + (3,0):'tested', (4,0):'sync\nOK', } for (col, row), label in result_labels.items(): ax.text(col, row, label, ha='center', va='center', @@ -486,7 +471,6 @@ for (col, row), label in result_labels.items(): for col in range(len(tests)): ax.text(col, 1, 'โœ…', ha='center', va='center', fontsize=12) - ax.text(col, 2, 'โœ…', ha='center', va='center', fontsize=12) plt.colorbar(im, ax=ax, fraction=0.02, pad=0.02).set_label( 'Pass Score', color='#c9d1d9', fontsize=9) @@ -548,7 +532,7 @@ print("Saved: charts/ram_by_target.png") --- -## ๐Ÿงช Phase-10 Global Hardening Report +## ๐Ÿงช Global Hardening Report > "Tested to destruction, proven in silence." @@ -558,24 +542,23 @@ print("Saved: charts/ram_by_target.png")
-| Attack Vector | Abuse Scenario | Claimed Result | Evidence Status | +| Attack Vector | Abuse Scenario | Result | Evidence Status | |:-------------|:---------------|:-------|:---------| | **CPU Saturation** | 100% load ร— 60 min | 0 latency spikes | ๐ŸŸก Test exists, reduced runtime | | **Memory Starvation** | 1GB free / 8GB total | 0 crashes, 0 leaks | ๐ŸŸก Conservative limits | | **Security Hammer** | 21 destructive payloads | **100% blocked** | โœ… Verified | -| **Flood Attack** | 25 req/s burst | 5.81ms avg latency | ๐ŸŸก Conservative thread count | +| **Flood Attack** | 25 req/s burst | Tested | ๐ŸŸก Conservative thread count | | **Time Warp** | 4 clock-drift extremes | Sync preserved | โœ… Verified | -| **ACPI Hibernation** | 50 rapid cycles | Wake-word intact | ๐Ÿ”ด Not implemented | -| **Thermal Throttle** | 85ยฐC SoC | 3.72ms max latency | ๐Ÿ”ด Not implemented | -| **EMI Chamber** | 30 V/m RF noise | 99.4% accuracy | ๐Ÿ”ด Not implemented | +| **ACPI Hibernation** | 50 rapid cycles | Not tested | ๐Ÿ”ด Not implemented | +| **Thermal Throttle** | 85ยฐC SoC | Not tested | ๐Ÿ”ด Not implemented | +| **EMI Chamber** | 30 V/m RF noise | Not tested | ๐Ÿ”ด Not implemented |
-### Certification Summary +### Test Summary ``` โš ๏ธ 6 / 8 torture tests implemented (EMI, Thermal, ACPI missing) -โš ๏ธ Phase-10: SELF-CERTIFIED (no external validation) โœ… Security effectiveness: 100% (on implemented tests) ๐Ÿ“Š Full reality check: tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md ``` @@ -609,16 +592,16 @@ Invoke-Item tests/reports/PERFORMANCE_CLAIMS_VERIFICATION.md --- -## ๐Ÿ† Leaderboard โ€” Latency vs Privacy vs Accuracy +## ๐Ÿ† Leaderboard โ€” Latency vs Privacy
-| System | Latency | Privacy | Accuracy | Deployment | -|:-------|:--------|:--------|:---------|:-----------| -| **Edge-TinyML** | **3.64ms** | **100% offline** | **99.6%** | MCU โ†’ Desktop โ†’ Server | -| Alexa | 200โ€“500ms | Cloud-only | ~95% | Cloud | -| Snowboy | 10โ€“20ms | Offline | ~90% | Embedded | -| Porcupine | 15โ€“30ms | Offline | ~92% | Embedded | +| System | Latency (Measured) | Privacy | Deployment | +|:-------|:--------|:--------|:-----------| +| **Edge-TinyML** | **~17ms** (dev) | **100% offline** | MCU โ†’ Desktop โ†’ Server | +| Alexa | 200โ€“500ms | Cloud-only | Cloud | +| Snowboy | 10โ€“20ms | Offline | Embedded | +| Porcupine | 15โ€“30ms | Offline | Embedded |
@@ -703,7 +686,7 @@ Generate PDF handbook: `make pdf` inside `docs/` โ†’ `Edge-TinyML-Handbook.pdf` - **Cognitive Model**: TinyLlama 1.1B GGUF (Q4 quantized) โ€” MIT-compatible weights - **External Calls**: None โ€” 100% offline inference, zero network traffic (packet-sniffer verified) - **Dataset**: Google Speech Commands (open license). Third-party fine-tunes may require CC-BY-NC. Run `scripts/check_weights_license.sh` to verify. -- **Known Limitations**: KWS accuracy validated at 3.64ms on benchmark hardware. Performance on low-end MCUs (ESP32 < 240MHz) may vary. EMI robustness tested at 30 V/m; higher RF environments require field validation. +- **Known Limitations**: KWS latency measured at ~17ms on current Windows/NumPy setup. Performance on embedded hardware requires production TFLite INT8 deployment. EMI robustness testing incomplete. --- @@ -711,10 +694,10 @@ Generate PDF handbook: `make pdf` inside `docs/` โ†’ `Edge-TinyML-Handbook.pdf` | Partner | Contribution | Impact | |:--------|:------------|:-------| -| [Google Speech Commands](https://www.tensorflow.org/datasets/catalog/speech_commands) | Training dataset | 99.6% KWS accuracy | +| [Google Speech Commands](https://www.tensorflow.org/datasets/catalog/speech_commands) | Training dataset | KWS model training | | [TensorFlow Lite](https://www.tensorflow.org/lite) | Micro-runtime | 77KB model possible | | [TinyLlama](https://github.com/jzhang38/TinyLlama) | 1.1B GGUF weights | On-device cognition | -| TinyML Community | Benchmarks & methodology | Phase-10 hardening | +| TinyML Community | Benchmarks & methodology | Hardening guidance | --- @@ -735,11 +718,11 @@ scripts/check_weights_license.sh **Genius-Level Intelligence, Zero Cloud Dependencies.** -*100% OFF-GRID ยท 3.64ms ยท 99.6% ยท 21/21 blocked ยท Phase-10 Certified* +*100% OFF-GRID ยท ~17ms (dev) ยท 21/21 blocked ยท 6/8 torture tests* โญ Star the repo ยท ๐Ÿ› Open an issue ยท ๐Ÿ”ง Submit a PR ยท ๐Ÿš€ Ship a product -[๐Ÿš€ Quick Start](#-quick-start) ยท [๐Ÿง  Architecture](#-genius-level-hybrid-architecture) ยท [๐Ÿ›ก๏ธ Security](#๏ธ-security-hardening-phase-10-certified) ยท [๐Ÿงช Torture Tests](#-phase-10-global-hardening-report) +[๐Ÿš€ Quick Start](#-quick-start) ยท [๐Ÿง  Architecture](#-genius-level-hybrid-architecture) ยท [๐Ÿ›ก๏ธ Security](#๏ธ-security-hardening) ยท [๐Ÿงช Hardening](#-global-hardening-report) *Built by [Ariyan-Pro](https://github.com/Ariyan-Pro)*