Overview of AI Chip Types, Architectures, and Market Trends

Broadly speaking, any chip that can run AI algorithms is called an AI chip, including CPUs, GPUs, FPGAs, NPUs, and ASICs, but they differ greatly in execution efficiency. CPUs can quickly perform complex math but their performance degrades with multitasking, making them unsuitable for AI workloads.

The heterogeneous CPU+xPU solution has become the standard for high‑compute scenarios, with GPUs being the most widely used AI chips. In China’s 2021 AI chip market, GPUs held an 89% share.

NPUs (Neural Processing Units) are domain‑specific architectures designed for neural network acceleration. Examples include Huawei’s Kirin 970 NPU, Google’s TPU, and Tesla’s FSD chip. NPUs achieve significant speedups in image‑recognition tasks and are increasingly integrated into SoCs for smartphones, cars, and security cameras.

Google’s TPU is a matrix processor specialized for neural‑network workloads, featuring a systolic array of multipliers and adders that performs massive multiply‑accumulate operations without frequent memory accesses, resulting in high throughput with low power consumption.

While CPUs and GPUs are general‑purpose processors with high memory‑access overhead, TPUs and NPUs are purpose‑built for AI, offering higher compute density and energy efficiency. The systolic array concept, originally proposed in 1982, was revived by Google in 2017 for TPUs and is now adopted by many vendors.

In the AI acceleration market, NVIDIA leads with an 82% share in 2022, dominating both training and inference segments.

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