AI Chip Landscape: Architecture, Trends, and Market Players

The AI chip series consists of six parts that examine the current state of the chip industry, the four essential elements of AI (data, compute, algorithms, and scenarios), and detailed analyses of GPU, FPGA, ASIC, and neuromorphic chips, as well as industry applications, technology trends, and major players.

Deep learning requires massive parallel computation; breakthroughs in big data, compute power, and training methods have propelled AI chips to the forefront of the upstream AI supply chain.

Traditional CPUs, with their serial architecture, cannot meet the parallelism demands of AI workloads, leading to the rise of AI‑specific hardware. GPUs were the first widely adopted solution due to their programmability and parallel performance.

AI chips can be classified by architecture: under the von Neumann model are non‑brain‑inspired chips such as CPUs and GPUs; non‑von Neumann designs include brain‑inspired and non‑brain‑inspired chips, the latter encompassing ASICs (e.g., Cambricon, Google TPU), FPGAs, and newer GPUs like Nvidia’s Tesla series.

Each chip type has distinct strengths: GPUs offer high peak performance and versatility but consume more power; FPGAs provide flexibility and efficiency at higher cost and lower peak performance; ASICs deliver high efficiency and low power at the expense of high upfront NRE costs; neuromorphic chips excel in low‑power perception but lack training accuracy.

Recent developments such as Nvidia’s Volta architecture, Intel’s CPU + FPGA integration, and Cambricon’s ASIC advancements suggest GPUs will continue to dominate high‑end training, while ASICs are poised for rapid adoption in intelligent terminals and AI platforms.

Globally, the ICT sector relies heavily on a strong chip foundation, with companies like Google, IBM, Intel, Nvidia, Samsung, and Sony contributing billions in profit. China’s chip ecosystem is catching up, highlighted by the rapid growth of firms such as Huawei’s HiSilicon, Cambricon, and other domestic designers, which have achieved double‑digit sales CAGR and are poised to enter the global top‑20.

Domestic ASIC breakthroughs, such as the first embedded NPU from Zhongxing Micro and successive generations of Cambricon’s DIANNAO chips, demonstrate China’s increasing capability to produce high‑performance, low‑power AI processors for smartphones, wearables, security cameras, and other edge devices.

The convergence of abundant data from smart devices, expanding compute resources, and mature deep‑learning algorithms creates a virtuous cycle that accelerates AI chip innovation and market adoption.