AI Accelerator
An AI accelerator is a processor built for one job: the matrix multiplications that neural networks are made of. GPUs, TPUs and NPUs are all accelerators; they trade the flexibility of a CPU for far higher throughput per watt on AI workloads.
No jargon, no math
In plain words, what is it?
A general-purpose CPU is a Swiss army knife: it runs one thread of instructions superbly, switching tasks constantly. Neural networks do something else: they multiply enormous grids of numbers against each other, the same operation millions of times with no decisions in between. An AI accelerator is a chip shaped exactly around that one shape of work. Thousands of small multipliers run in parallel, fed by memory sitting close by, under a control unit that never has to guess what comes next. The result: a model that took a CPU minutes returns in seconds, at a fraction of the energy. Where it breaks: flexibility. Ask an accelerator to run a spreadsheet or a database and it sits nearly idle. It always works as a sidekick to a normal processor, never a replacement.
Terms that live nearby.
GPU
A GPU is a processor built to run thousands of small calculations at the same time. Designed for graphics, it turned out to be the perfect engine for AI, because running a neural network is mostly one enormous pile of matrix multiplications.
ReadTPU
A TPU is Google's custom chip built for a single job: the matrix multiplications at the heart of neural networks. You do not buy it; you rent it by the hour inside Google Cloud, where it often beats general-purpose GPUs on training cost.
ReadNPU
An NPU is a neural processing unit: a low-power AI accelerator built into laptops, phones and industrial devices, so that inference runs on the device itself with no round trip to the cloud.
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