GlobalData has used its exclusive theme-based analysis to identify the semiconductor companies which are best placed to succeed in the future based on their strength in artificial intelligence (AI).

AI has been identified by GlobalData as one of the top ten themes that will impact the semiconductor sector over the next two years.

The other nine are: high-performance computing, data centres, ambient commerce, self-driving cars, industrial internet, gaming, 5G, M&A and geopolitics.

The semiconductor companies given the highest thematic weighting by GlobalData for AI are: NVIDIA, Google, Apple, Tesla, Xilinx, STMicroelectronics, Texas Instruments, AMD, ASML, Baidu, ROHM, Graphcore and Cambricon.

GlobalData’s thematic research team have developed a “thematic engine” which is designed to identify tomorrow’s tech leaders rather than today’s tech incumbents, based on their competitive position in the most important themes impacting their industry, such as AI.

The GlobalData thematic engine spans 15 key technology sectors – from semiconductors to consumer electronics to enterprise software – and covers over 60 themes.

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What does AI mean for the semiconductor sector?

AI systems need to process massive amounts of data very quickly. While the performance of general-purpose chips has improved sufficiently to kick-start a new generation of AI technology, they cannot keep up with the dramatic growth in the AI sector, along with the accompanying exponential increase in the volume of data that AI systems process.

This has given rise to the advent of hardware-based acceleration via algorithmic-specific chips such as field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) that allow developers to explore lucrative but resource-intensive use cases like real-time facial recognition, automobile collision avoidance or responsive marketing.

There is a high degree of specialization among available acceleration chip architectures, each focusing on solving performance problems specific to different use cases, deployment environments, and even phases of the AI application lifecycle (modelling, training, inference, optimization, etc.). This specialization and the popularity of AI itself has led to an arms race among chip manufacturers seeking to solve both narrow and wide AI problems on mobile devices, at the network edge, in the data center and on the public cloud platform.