Hardware Constraints and the Need for Optimization
The contemporary AI landscape is defined by a reliance on high-throughput parallel processing units (GPUs). The "GPU hegemony," characterized by the dominance of the NVIDIA CUDA ecosystem, has created constraints regarding cost, energy consumption, and supply chain availability.
A transition is occurring toward reimagining the fundamental mathematics of deep learning. By moving from dense matrix multiplication to sparse hash-based searches and integer arithmetic, it is possible to leverage the serial processing strengths and large memory hierarchies of modern CPUs.
This report explores the innovations decoupling machine learning from GPU dependency, from ThirdAI's BOLT engine to the democratization of inference through llama.cpp and Rust-based ecosystems. The analysis suggests a future where AI becomes ubiquitous on existing infrastructure, defying Moore's Law and the Jevons Paradox.
Alternative Hardware Paradigms
- Analog Computing: A historical paradigm regaining relevance for its potential to perform vector-matrix multiplications at near-zero energy cost compared to digital logic.
- FPGAs (Field-Programmable Gate Arrays): Utilizing high-level synthesis (HLS) to create custom logic paths for specific model architectures, providing a middle ground between CPU flexibility and ASIC efficiency.
- Processing-in-Memory (PIM): Architectures that integrate logic directly into the DRAM or SRAM, eliminating the "memory wall" bottleneck that affects both CPUs and GPUs.
- Optical Computing: Research into using light instead of electrons for neural network activations, targeting terahertz-scale processing speeds.