Structured Sparse Energy Transformer (SSET): A Low-Compute Brain-Inspired Language Model for CPU-Efficient Reasoning

SSET is designed to reduce the compute, memory bandwidth, and energy demands of dense Transformer language models. It combines low-rank query-key-value projections, structured sparse attention, multi-channel contextual attention, adaptive energy-based reasoning, laminar refinement, memory-conditioned retrieval, and two-stage candidate selection.

• Replaces dense token-to-token attention with structured sparse interaction over active regions
• Uses low-rank projections to reduce parameter and memory complexity
• Targets CPU-only and edge-device language-model inference without relying on large GPU systems

1. 1Implements token embedding, low-rank projection, sparse attention, laminar refinement, and energy-based decoding
2. 2Restricts attention to top-k relevant token interactions and candidate-limited inference paths
3. 3Uses Python and PyTorch as the implementation environment described in the paper

• The architecture is designed to reduce computation by routing work only through structured sparse regions
• Energy-based candidate ranking is used as an alternative to dense full-vocabulary projection
• The paper presents SSET as a direction for energy-efficient, biologically inspired language intelligence

SSET proposes a low-compute Transformer alternative that combines structured sparsity and energy-based reasoning for more accessible CPU-efficient language modeling.