| # SRS.md | |
| ## Project: G-Transformer | |
| ### Version: 1.0 | |
| ### Title: Energy-Efficient Transformer Architecture Based on Genesis Information Theory (GIT) | |
| ### Author: Syamsuddin B. Ideris, S.Pd.MM | |
| ### Institution: SMPN 3 Kandangan – Independent Researcher | |
| --- | |
| ## 1. Introduction | |
| ### 1.1 Purpose | |
| Dokumen ini menjelaskan kebutuhan dan rancangan arsitektur **G-Transformer**, model Large Language Model (LLM) hemat energi berdasarkan **Genesis Information Theory (GIT)**. Tujuannya adalah mengurangi konsumsi daya komputasi AI dengan memandang seluruh proses neural network sebagai **transfer dan transformasi informasi (I)** yang tunduk pada hukum energi-informasi (E = k_I \cdot T \cdot I). | |
| ### 1.2 Scope | |
| G-Transformer adalah varian arsitektur Transformer yang: | |
| 1. Mengoptimalkan efisiensi energi per token (J/token). | |
| 2. Meminimalkan operasi berlebih melalui mekanisme **Informational Attention (IA-Attention)**. | |
| 3. Mengompresi cache dan mengurangi redundansi data berdasarkan **kontribusi informasi ΔI**. | |
| 4. Mengatur konsumsi daya GPU melalui kontrol **DVFS (Dynamic Voltage and Frequency Scaling)** berbasis laju informasi. | |
| 5. Menjaga akurasi dan stabilitas seperti Transformer konvensional. | |
| ### 1.3 Objectives | |
| * Mengurangi energi inferensi dan pelatihan hingga 80%. | |
| * Meningkatkan rasio **FLOPS/Watt** hingga 3× dibanding FP16 Transformer standar. | |
| * Menghasilkan model yang lebih efisien secara termodinamika, tanpa kehilangan akurasi signifikan. | |
| --- | |
| ## 2. Theoretical Foundation | |
| ### 2.1 Core Principle | |
| GIT memandang energi dan informasi sebagai ekuivalen: | |
| [ | |
| E = k_I , T , I | |
| ] | |
| di mana (k_I) adalah konstanta informasi fundamental. | |
| ### 2.2 Information Flow Equation | |
| Total energi operasi model dinyatakan sebagai: | |
| [ | |
| E_{\text{total}} = N_{\text{ops}} E_{\text{op}} + N_{\text{bytes}} E_{\text{bit}} + E_{\text{idle}} | |
| ] | |
| Efisiensi informasi dihitung sebagai: | |
| [ | |
| \eta_I = \frac{I_{\text{useful}}}{I_{\text{total}}} | |
| ] | |
| dan tujuan optimasi adalah memaksimalkan (\eta_I) dengan batasan kehilangan akurasi minimum. | |
| --- | |
| ## 3. Functional Requirements | |
| ### 3.1 Informational Attention (IA-Attention) | |
| * Menghitung **ΔI per token** sebagai ukuran relevansi. | |
| * Mengabaikan koneksi antar token dengan ΔI di bawah ambang ε. | |
| * Kompleksitas efektif: O(n·w + r·n). | |
| * Mengurangi operasi atensi hingga 10×. | |
| ### 3.2 Low-Rank Feed Forward Network (LR-FFN) | |
| * Faktorisasi matriks bobot (W = UΣV^T). | |
| * Sparsity 2:4 pada bobot dan aktivasi. | |
| * Presisi FP8 untuk matmul, FP16 akumulasi. | |
| * Penghematan energi 2–3× dibanding FFN standar. | |
| ### 3.3 Entropy-Based MoE Router | |
| * Aktivasi expert hanya jika ΔI_expert ≥ ε_expert. | |
| * Fungsi penalti: | |
| [ | |
| L_{\text{info}} = λ \cdot I_{\text{waste}} | |
| ] | |
| * Mengurangi FLOPS tanpa degradasi kualitas. | |
| ### 3.4 KV-Cache Compression | |
| * Hitung skor informasi (r = ||k|| \cdot ||v||). | |
| * Simpan hanya vektor dengan r > ε_kv. | |
| * Memori KV turun 2–8×. | |
| ### 3.5 Delta-Gradient Communication | |
| * Kirim hanya perubahan gradien signifikan: | |
| [ | |
| Δg = g_t - g_{t-1} | |
| ] | |
| * Kompresi INT8 dan entropi coding. | |
| * Penghematan energi komunikasi hingga 80%. | |
| ### 3.6 DVFS & Information Scheduler | |
| * Sensor energi dan suhu membaca σ lokal GPU. | |
| * Tegangan diturunkan terlebih dahulu sebelum frekuensi. | |
| * Pembagian daya: compute 60%, memory 25%, I/O 10%, control 5%. | |
| * Scheduler menyeimbangkan panas dan laju informasi. | |
| --- | |
| ## 4. Non-Functional Requirements | |
| | Category | Description | | |
| | --------------- | ----------------------------------------------------------------------- | | |
| | Performance | Latency ≤ 1.2× Transformer FP16, dengan konsumsi daya ≤ 0.2× | | |
| | Scalability | Dapat berjalan pada GPU single node hingga cluster multi-node | | |
| | Reliability | Self-adaptive terhadap beban data variatif | | |
| | Maintainability | Modular (Attention, FFN, Cache, MoE, Scheduler) | | |
| | Portability | Kompatibel dengan PyTorch, TensorFlow, dan framework C++ custom | | |
| | Energy | Efisiensi minimum 0.1 J/token (inferensi), 0.5 kWh/epoch (training 13B) | | |
| --- | |
| ## 5. System Architecture | |
| ### 5.1 Main Components | |
| 1. **IA-Attention Module** – menyeleksi token informatif. | |
| 2. **LR-FFN** – menghemat operasi linear. | |
| 3. **KV-Compressor** – kompresi cache. | |
| 4. **Entropy-MoE Router** – routing berbasis kontribusi ΔI. | |
| 5. **Delta-Gradient Engine** – hemat komunikasi. | |
| 6. **DVFS Controller** – optimasi energi runtime. | |
| 7. **Information Scheduler** – menjaga keseimbangan beban dan suhu. | |
| ### 5.2 Energy Flow | |
| [ | |
| E_{\text{total}} = E_{\text{compute}} + E_{\text{memory}} + E_{\text{I/O}} + E_{\text{control}} | |
| ] | |
| Target reduksi (E_{\text{total}}) hingga 70–85%. | |
| --- | |
| ## 6. Hardware Requirements | |
| | Component | Specification | | |
| | ------------ | --------------------------------------------------- | | |
| | GPU | NVIDIA H100 / MI300X / RTX 4090 | | |
| | Memory | ≥ 96 GB HBM3 | | |
| | Storage | NVMe SSD 4 TB | | |
| | Cooling | Gitton Cooling System (GCS) hybrid fluid-electronic | | |
| | Power Supply | 2.4 kW efficiency 94% | | |
| | Sensors | Power, temperature, entropy (software metric) | | |
| --- | |
| ## 7. Data and Training | |
| ### 7.1 Dataset | |
| * Text corpus open-source (Pile, RedPajama, OSCAR). | |
| * Metadata tambahan: entropy score dan ΔI log per batch. | |
| ### 7.2 Training Objectives | |
| [ | |
| L_{\text{total}} = L_{\text{crossentropy}} + λ L_{\text{info}} | |
| ] | |
| dengan: | |
| [ | |
| L_{\text{info}} = λ \cdot (I_{\text{total}} - I_{\text{useful}}) | |
| ] | |
| ### 7.3 Monitoring Metrics | |
| * J/token | |
| * FLOPS/Watt | |
| * Memory Access Efficiency | |
| * ΔEntropy per Layer | |
| * Accuracy Delta (Δloss) | |
| --- | |
| ## 8. Verification and Testing | |
| | Test Type | Description | Metric | | |
| | ----------- | --------------------------------- | ------------------------- | | |
| | Unit Test | Validasi modul IA-Attention & FFN | Accuracy ±1e-5 | | |
| | Integration | Sinkronisasi ΔI antar modul | Stability over 10k tokens | | |
| | Energy Test | Bandingkan J/token | ≤ 0.1 J/token | | |
| | Stress Test | Context 64k token | No overflow | | |
| | Robustness | Simulasi noise data | Δloss ≤ 0.5% | | |
| --- | |
| ## 9. Future Roadmap | |
| 1. Integrasi dengan **GitPU** (GPU berbasis GIT). | |
| 2. Implementasi hardware-aware compiler untuk DVFS. | |
| 3. Adaptasi ke model multimodal (text, image, speech). | |
| 4. Kolaborasi dengan laboratorium energi rendah AI. | |
| --- | |
| ## 10. References | |
| 1. Syamsuddin B. Ideris – *Genesis Information Theory (GIT v2.0)*, 2025. | |
| 2. Vaswani et al. – *Attention Is All You Need*, 2017. | |
| 3. OpenAI & NVIDIA whitepapers on FP8 training, 2023–2024. | |
| 4. MIT CSAIL – *Energy-aware Neural Architecture Design*, 2022. | |
| --- | |