A Decade of Architectural Evolution: A Comprehensive Survey of Deep Learning Model Implementations
DOI:
https://doi.org/10.63125/y7e5vr53Keywords:
Deep Learning, Transformer Architecture, Mixture of Experts, Test-Time Compute, Systems Co-design, Attention TaxonomyAbstract
This comprehensive survey paper presents a rigorous, systematic evaluation of deep learning model implementations, engineering milestones, and cross-layer architectural transformations over the highly transformative decade spanning from 2016 to 2026. During this chronological period, the global artificial intelligence paradigm shifted decisively from highly heterogeneous, task-specific convolutional configurations and recurrent layer hierarchies to standardized, uniform, global Transformer-based execution pipelines, ultimately consolidating into contemporary sparse Mixture of Experts (MoE) topologies and multi-stage test-time compute reinforcement layouts. We map out the granular mathematical and systemic developments that enabled this massive scaling behavior, focusing heavily on core attention mechanism modifications (including Multi-Head, Multi-Query, Grouped-Query, and Multi-head Latent Attention structures), performance-driven layer normalization changes (such as LayerNorm and RMSNorm frameworks), and rotary positional transformations. Additionally, we analyze system-level hardware-aware engineering breakthroughs that successfully mitigated physical compute boundaries, highlighting critical memory tiling strategies like FlashAttention, sharded optimizer parameters via ZeRO optimization pipelines, and mixed-precision low-bit computation layouts (FP8/FP4 mixed tensor configurations). Finally, we characterize the latest 2025–2026 operational shift toward dynamic test-time scaling systems, where inference-phase processing budgets scale fluidly with automated verification chains, tree search methods, and self-correction loops. By structuring this comprehensive multi-layer technological taxonomy, this survey traces the critical structural, co-designed software-hardware execution pipelines that form the foundational baseline of modern generative artificial intelligence networks across global deployment landscapes.
