Despite the enormous speed of processing reams of data and providing a valuable output, artificial intelligence applications have one key weakness—their brains are located thousands of miles away (on the cloud). Over the last few years, edge computing and artificial intelligence have been brought together to form “Edge AI,” harnessing respective benefits while creating new opportunities as a result of their combination. The market for edge AI processors is still new, but the ever-increasing quest for faster computation at lower power consumption offers immense opportunities for AI hardware market.
The global Edge AI hardware market is projected to grow from 1,056 Million Units in 2022 and expected to reach 2,716 million units by 2027; it is expected to grow at a CAGR of 20.8% during the forecast period.
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The real-time computational ability of Edge AI is reflected in real-world use cases, resulting in massive, predicted growth. According to market research firm MarketsandMarkets, the worldwide edge AI market will reach 2,716 million units in 2027 at a compound annual growth rate (CAGR) of 20.8% over the 2022–2027 forecast period. CPU accounted for the largest market share in 2022, followed by ASIC and GPU.
At this stage of the market, there are very limited applications of FPGA for edge AI. However, autonomous driving companies may opt for FPGAs for inference. It is more likely that FPGAs will be used for training purposes in applications that require significant customization. Face detection, image /speech recognition, object/pose detection are some of the applications where FPGAs can be used. Intel, Xilinx, and Lattice Semiconductor are some of the major companies providing FPGA for edge AI applications. However, they are mostly limited to developers at present.
Most AI algorithms need enormous amounts of data and computing power to accomplish tasks. For this reason, they rely on cloud servers to perform their computations and cannot accomplish this on the device or at the edge, smartphones, computers, and other devices where the applications that use them run. This limitation makes cloud-based AI algorithms useless or inefficient in settings where connectivity is sparse or non-present, and where operations need to be performed in real time, such as autonomous vehicles.
The connectivity in mobile devices suffers from latency, network congestion, signal collisions, and terrain. These are the challenges we face when processing edge data in the cloud. The dedicated AI processor in mobile devices can help compute resources in real time and execute algorithms without the need for a round-trip to the cloud. Another benefit of using dedicated AI processors is the incentivization of resource sharing. Apart from smartphones, surveillance cameras, augmented reality, robots, and autonomous/driverless cars are applications where real-time intelligence at the edge is crucial. The delay caused by the round-trip to the cloud can yield disastrous or even fatal results, and in case of a network disruption, a total halt of operations is imaginable.
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