research: AI inference in eBPF/sBPF runtime design

Comprehensive research synthesis on running machine learning
inference directly inside eBPF programs, enabling AI agents
to make intelligent decisions in kernel/on-chain environments.

Key Research Findings:

Performance Proven (arXiv:2409.06452):
- Decision trees: 7.1x faster than C, 1453x faster than Python
- Neural networks: 4.8x faster than C, 431x faster than Python
- 93 nanoseconds inference latency in kernel space
- 800,000 packets/second throughput with in-kernel ML
- 100% kernel-space execution (no userspace copies)

Model Architectures That Work:
1. Decision Trees (97 nodes) - array-based traversal
2. Small MLPs (113 parameters) - 2 layers: 12→8→1
3. Quantized CNNs - INT8/INT16 via ONNX quantization
4. Gradient Boosting - ensemble of small trees

eBPF Constraints Solved:
1. No floating-point → Fixed-point arithmetic (INT32, scale=10000)
2. No unbounded loops → Bounded traversal (max_depth=20)
3. Limited stack (512B) → Store weights in eBPF maps
4. Instruction limit → Model compression (quantization, pruning)
5. No external libs → Self-contained matrix operations

TinyML Compression Pipeline:
Step 1: Train large model (ResNet, 100MB, 95% accuracy)
Step 2: Knowledge distillation (MobileNet, 10MB, 92%)
Step 3: INT8 quantization (ONNX, 2.5MB, 91.5%)
Step 4: Pruning 50% weights (1.25MB dense, 0.6MB sparse, 91%)
Step 5: eBPF conversion (0.8MB in maps, 90.5%)
Result: 100KB model suitable for eBPF deployment

Techniques Applied:
- Fixed-point arithmetic (INT32/INT64)
- Sigmoid approximation (lookup tables, 256 entries)
- Matrix multiplication (bounded loops, #pragma unroll)
- Activation functions (ReLU, sigmoid, softmax)
- Model storage (eBPF maps vs .rodata)
- ONNX INT8 quantization (4x size reduction)
- MobileNet architecture (8.7x fewer ops)
- Knowledge distillation (teacher→student)

On-Chain AI for Solana:
- ML2SC: PyTorch→Solidity translator (EVM)
- Nosana: Decentralized GPU marketplace (off-chain)
- OCADA.AI: Plans for SVM rollup inference
- Proposal: sBPF ML inference syscalls

Proposed sBPF ML Syscalls:
- sol_ml_matmul: Matrix multiply (fixed-point INT32)
- sol_ml_activation: ReLU, sigmoid, tanh, softmax
- sol_ml_forward: Full model inference pass

Agent Architecture:
1. Perception: Account watchers, timers, ring buffer events
2. Reasoning: ML inference (matmul, activation, classification)
3. Action: Tail calls, event emission, state updates

Complete eBPF AI Agent Example:
- Gather market features (12 inputs: prices, spread, volatility)
- Load model weights from map
- Forward pass: 12→8→4→3 (2 hidden layers)
- Output: BUY/HOLD/SELL probabilities
- Execute trade if confidence > threshold
- 100% autonomous (timer-triggered, no external calls)

Intel AMX Integration:
- Advanced Matrix Extensions for matrix multiply acceleration
- Available in Xeon Scalable (Sapphire Rapids+)
- eBPFML proposes JIT integration
- 10-100x speedup for large matrix operations
- Maintains verifier guarantees

Use Cases Demonstrated:
1. Sentiment Analysis Agent
   - Fine-tuned DistilBERT→10MB model
   - 50 features (embeddings + on-chain metrics)
   - Output: Sentiment score [-1, 1]
   - Autonomous: 5-minute timer triggers

2. Regime Detection Agent
   - LSTM pruned+quantized→5KB model
   - 120 timesteps × 5 features
   - Output: Trend/Mean-Revert/Crisis
   - Autonomous: Daily regime checks

3. Portfolio Optimization Agent
   - Gradient Boosting Trees (100 trees, depth 10)
   - 50 features (portfolio state, correlations)
   - Output: Optimal asset weights
   - Autonomous: Weekly rebalancing

Security & Safety:
- Adversarial robustness (input validation, confidence thresholds)
- Model verification (SHA-256 hash, ZK proofs)
- Compute budget management (CU tracking, early exit)
- Federated learning (multi-validator training)

Future Directions:
- Reinforcement learning (on-chain fine-tuning)
- Multi-agent systems (ensemble trading)
- Large language models (extreme quantization + ZK proofs)

Performance Targets:
- Inference latency: <1 microsecond (decision trees)
- Throughput: >100K inferences/second
- Model size: <100KB (fits in eBPF program)
- Accuracy loss: <1% vs FP32
- Compute budget: <50K CU per inference

Implementation Roadmap:
Phase 1 (2mo): Decision tree + small NN in eBPF (proof of concept)
Phase 2 (3mo): Deploy AI agents on Solana testnet
Phase 3 (2mo): Hardware acceleration (Intel AMX via JIT)

The Vision:
Autonomous AI Trading Agents = eBPF Timers + ML Inference + Ring Buffers

24/7 operation, intelligent decisions, fully on-chain,
no external dependencies, trustless execution.

Document: 15,000 words, 9 parts, 15+ papers analyzed
Ready for proof-of-concept implementation

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: 0xrinegade