Hardware Acceleration
Using specialized hardware (GPUs, TPUs, FPGAs, ASICs) to speed up AI computation compared to general-purpose CPUs. Accelerators are optimized for the specific math operations used in neural networks.
Why It Matters
Hardware acceleration has reduced AI training costs by 1000x over a decade. The competition between accelerator providers drives the pace of AI progress.
Example
Training a model in 3 days on GPUs that would take 3 years on CPUs — the same computation, but specialized hardware makes it practical.
Think of it like...
Like using a dishwasher instead of hand-washing — specialized equipment handles the specific task dramatically faster than general-purpose effort.
Related Terms
GPU
Graphics Processing Unit — originally designed for rendering graphics, GPUs excel at the parallel mathematical operations needed for training and running AI models. They are the primary hardware for modern AI.
TPU
Tensor Processing Unit — Google's custom-designed chip specifically optimized for machine learning workloads. TPUs are designed for matrix operations that are fundamental to neural network computation.
CUDA
Compute Unified Device Architecture — NVIDIA's parallel computing platform that enables GPU programming for AI workloads. CUDA is the dominant software ecosystem for AI computation.
ASIC
Application-Specific Integrated Circuit — a chip designed for a single specific purpose. In AI, ASICs like Google's TPUs are designed exclusively for neural network operations.
Compute
The computational resources (processing power, memory, time) required to train or run AI models. Compute is measured in FLOPs (floating-point operations) and is a primary constraint and cost in AI development.