In its new technology & market report, ‘Artificial Intelligence Computing for Consumer’, Yole identifies numerous and impressive consumer applications. Indeed biometrics, surveillance, photography, remote control, virtual assistant are all part of the AI for consumer ecosystem.
“AI for consumer is both invisible and yet so present,” said Yohann Tschudi, Technology & Market Analyst, Computing & Software at Yole Développement (Yole). “AI enters our daily life as a wonderful tool to interact with our environment.”
Without doubts, AI impact remains immense. At Yole, analysts estimate 2024 total market for hardware computing for consumer applications to reach $15.6bn. This market can be shared between two market segments: the stand-alone chip at $ 3.8bn and the embedded one, especially supported by the smartphone, up to $11.8bn.
Aim of this analysis is to reveal an AI scenario with dynamics of the consumer industry. This report evaluates the AI impact on the semiconductor industry and proposes an in-depth understanding of the AI ecosystem and related players. Key technical insight and analysis into future technology trends and challenges are well detailed in Yole’s report. Yole’s analysts have anticipated for a few years now the move from the cloud to the edge.
Today transition is happening faster than ever, especially within the imaging market segment. Facing this disruption, AI algorithms require powerful hardware. Yole analyzes the status of AI technologies for consumer applications and reveals a detailed scenario of their implementation.
“In recent years, we are seeing the emergence of edge computing,” said Tschudi. “And this trend involves placing the calculation at the system level. However, the constraints are strong: always-on, consumption and performance in particular.”
With the end of Moore’s law and the need for power demanded by AI algorithms, it was necessary to create a new type of dedicated architecture. This unit has different names: deep learning accelerator, neural engine, neural processing unit, AI-processing unit. The goal is the same: to allow, without the need of a power-hungry GPU, to parallelize calculations very numerous in deep learning algorithms, and thus bring intelligence directly to the device level, independent of the cloud.
Yole’s report distinguishes two types of technologies: either the AI is entirely dedicated to the analysis due to a stand-alone chip, or the unit dedicated to this task is embedded in a SoC whose objective is not centered on the analysis.
To name a few examples, on the one hand, for the stand-alone chip, Intel Movidius is a perfect example. On the other hand, an “application processor”, like Qualcomm’s Snapdragon series, which is the central chip of smartphones containing a neural engine are representative of the embedded category.
“For each type of computing hardware, the actors are different and specialized,” explained John Lorenz, Technology & Market Analyst, Computing & Software at Yole.
Even if the IP players propose solutions for the whole ecosystem, it is obvious that there is a strong stake to bring the calculation close to the sensor or centralize it in a multifunction chip. In the first case, Yole finds the historical actors as well as the suppliers of sensors who want to add value to their product: for example, ON Semiconductor, Ambarella, TI, Sony, Knowles, ams.
On the other more and more OEMs also wants to capture this value by designating their own chip. Apple, Samsung, Huawei (with HI Silicon) and more famous players such as Intel or Qualcomm are part of this ecosystem. The latter stands out by offering this type of computing for other markets than the smartphone such as virtual personal assistant, drones or smart camera.
At the top of the pyramid, the tech giants are also designing their own hardware, especially at the level of cloud computing where the value is even stronger and the clear objective of the data, today equivalent to a currency, different.
