Comprehensive guide to NeuroZ AI's architecture, implementation, and technical specifications.
Advanced Neural Architecture:
- Scaled dot-product attention with O(n²d) complexity
- Multi-query attention optimization for inference
- Rotary positional embeddings (RoPE)
- Adaptive KV-caching with 8-bit quantization
- Flash Attention 2.0 implementation
1. Advanced Tokenization
- SentencePiece unigram LM tokenization
- Byte-level BPE with regex merging
- Learned positional embeddings
- Causal masked self-attention
2. Architectural Optimizations
- Grouped-query attention (GQA)
- Sparse attention patterns
- Mixture of Experts (MoE)
- Adaptive layer normalization
3. Inference Optimization
- Speculative sampling
- Dynamic batch processing
- Continuous batching
- Beam search with length penalties
Parameter Management:
- Distributed sharding with ZeRO-3
- 4-bit NormalFloat quantization
- Activation checkpointing
- Gradient accumulation
Memory Optimization:
- Paged attention mechanism
- Structured state management
- Prefetch queue optimization
- Page-level spilling
Inference Pipeline:
- Continuous batching engine
- Dynamic tensor parallelism
- Adaptive batch scheduling
- Pipeline parallelism
AST Processing:
- Incremental parsing with error recovery
- Type inference with constraint solving
- Cross-reference resolution
- Symbol table management
Generation Pipeline:
- Semantic-aware beam search
- Context-sensitive completion
- Multi-file dependency analysis
- Inheritance graph traversal
Training Pipeline:
- Distributed pre-training with DeepSpeed ZeRO-3
- Dynamic loss scaling with gradient accumulation
- Adaptive learning rate scheduling
- Mixed-precision training with bfloat16
Architecture Details:
- Multi-head attention with relative positional bias
- Gated cross-attention mechanisms
- Sparse expert routing with capacity factor 2
- Adaptive input/output embeddings
Evaluation Metrics:
- Perplexity analysis with sliding windows
- ROUGE-L and BLEU score computation
- Nucleus sampling evaluation (p=0.9)
- Length-normalized log probabilities
Robustness Testing:
- Adversarial prompt injection detection
- Input fuzzing with structured mutations
- Boundary testing with max sequence length
- Memory leak detection in attention cache
Performance Profiling:
- Kernel execution analysis with nsight
- Memory bandwidth utilization tracking
- Cache hit rate optimization
- Thread divergence analysis
The system leverages cutting-edge AI technologies with advanced optimizations: