The growth, says the firm, will be propelled by the increasing focus on low latency, advanced automation, and the availability of low-cost and ultra-power-efficient artificial intelligence (AI) chipsets - also known as "very edge" AI or embedded AI devices. These chipsets perform AI inference almost fully on board while continuing to rely on external resources - such as gateways, on-premise servers, or the cloud - for training.
As enterprises start to look for AI solutions in areas of voice activation, image or video screening, people tracking, and ambient tracking, says the firm, end-users struggle with the restricted nature of battery-powered sensors and embedded modules that operate on low computational resources offered by general-purpose microcontrollers. Often, edge sensors and devices need to handle large amounts of data, but due to the low powered nature of these devices they struggle to support high computing performance and high data throughput, causing latency issues.
"Since AI is deployed to make immediate critical decisions such as quality inspection, surveillance, and alarm management, any latency within the system may result in machine stoppage or slowdown causing heavy damages or loss in productivity," says Lian Jye Su, Principal Analyst at ABI Research. "Moving AI to the edge mitigates potential vulnerability and risks such as unreliable connectivity and delayed responses."
Featuring quantized AI models, TinyML chipsets enable smart sensors to perform data analytics on hardware and software dedicated for low powered systems, typically in the milliwatt range, using algorithms, networks, and models down to 100 kB and below. ARM and CEVA have both launched a chipset IP solution that supports low powered AI inference with supporting software libraries, toolchains, and models.
Low-powered AI chipset vendors including GreenWaves Technologies, Lattice Semiconductor, Rockchip, Syntiant, and XMOS have launched embedded AI chipset products in 2019. Realizing the potential of TinyML in machine vision, says the firm, CMOS vendors such as Sony and HiMax are also integrating TinyML chipset into their CMOS