Resource-Efficient LLM Architecture
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A Resource-Efficient LLM Architecture is a language model architecture that optimizes computational resource usage and memory footprint while maintaining language modeling capability for constrained deployment environments.
- AKA: Efficient LLM Design, Compact Architecture, Mobile LLM Architecture, Optimized Language Model Architecture, Lightweight Neural Architecture.
- Context:
- It can typically reduce parameter count through weight sharing mechanisms.
- It can typically employ Attention Optimization Techniques like linear attention.
- It can often utilize Model Compression Methods including quantization and pruning.
- It can often implement Knowledge Distillation Processes from teacher models.
- It can support Edge Device Deployments with limited memory budgets.
- It can integrate Mixture of Experts Architectures for conditional computation.
- It can employ Dynamic Neural Architectures with adaptive depth.
- It can range from being a Shallow Resource-Efficient LLM Architecture to being a Deep Resource-Efficient LLM Architecture, depending on its layer count.
- It can range from being a Dense Resource-Efficient LLM Architecture to being a Sparse Resource-Efficient LLM Architecture, depending on its parameter activation ratio.
- It can range from being a Fixed Resource-Efficient LLM Architecture to being a Adaptive Resource-Efficient LLM Architecture, depending on its runtime flexibility.
- It can range from being a Homogeneous Resource-Efficient LLM Architecture to being a Heterogeneous Resource-Efficient LLM Architecture, depending on its component diversity.
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- Example(s):
- Linformer Architecture, using linear complexity attention.
- Performer Architecture, with FAVOR+ attention mechanism.
- Reformer Architecture, using locality-sensitive hashing.
- Flash Attention Architecture, optimizing memory access patterns.
- Sparse Transformer Architecture, with factorized attention.
- Switch Transformer Architecture, using sparse MoE layers.
- ...
- Counter-Example(s):
- Dense Transformer Architecture, with full attention computation.
- Standard BERT Architecture, without efficiency optimizations.
- GPT-3 Architecture, prioritizing scale over efficiency.
- Unoptimized Neural Architecture, without resource constraints.
- See: Neural Architecture Design, Model Compression, Efficient Attention Mechanism, Lean Language Model, Mobile AI Architecture, Edge Computing, Sparse Neural Network, Knowledge Distillation, Neural Architecture Search.