With companies – from large corporates to startups and SMEs – vying to establish the fundamental AI accelerator technology that will support the AI ecosystem, the protection of intangible assets, including intellectual property (IP), will come to the forefront as one of the key aspects for success in this sector.
A huge increase in the size of ML models (roughly doubling about every 3.5 months) has been one of the key driving forces in the growth of ML model accuracy over recent years. In order to maintain this almost Moore’s-law growth in complexity, there is a clear demand in the market for new types of AI accelerators that can support more advanced ML models, for both training and inference.
One area of AI that would particularly benefit from new AI chips is AI inference at the edge. This relatively recent trend of running AI inference on a device itself, rather than on a remote (typically cloud) server offers many potential benefits such as removing latency in processing and reducing data transmission and bandwidth, and it may also increase privacy and security. In light of these advantages, the growth of the edge AI chips market has been remarkable - the first commercially available enterprise edge AI chip only launched in 2017, yet Deloitte predicts that more than 750 million edge AI chips will be sold in 2020.
The global AI chip market as a whole was valued at $6.64bn in 2018, and is projected to grow substantially in upcoming years, to reach $91.19bn by 2025, increasing at compound annual growth rate of 45.2%. Understandably, a wide range of companies are, therefore, working to develop AI chips. However, the market is poised to go through a growth cycle similar to those seen in the CPU, GPU and baseband processor markets, ultimately maturing to be dominated by a few large players. IP, and patents in particular, have been key to the success of household names such as Intel, Qualcomm and ARM, and it will likely play a similarly prominent role in the AI chip arena.