Resource-Efficient LLM Architecture

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.
  • ...
• 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.

---