AI at the edge for enterprises
Enterprises are eager to use intelligent artificial intelligence (AI) chips to bring data analytics workloads down to the edge of IoT networks.
In four years, the size of the market for edge AI chips is set to surpass the equivalent for cloud architectures, reaching $12.2 billion against $11.1 billion for the cloud, according to ABI Research.
During the COVID-19 pandemic, enterprise IoT deployment has accelerated as a means to overcoming the specific operational challenges encountered by organizations throughout the crisis.
This increased demand for enterprise IoT comes as the capabilities of connected things at the edge has expanded. With intelligent technologies that derive insights with fewer round trips to the cloud, the number of potential applications increases. In addition, the cost of bandwidth expended on cloud connectivity is reduced.
This matters to businesses because there’s a limit to what existing sensor networks can achieve, even in sectors where IoT adoption has been strong, such as in the industrial sector or the supply chain.
According to a study from University of Brescia, the typical industrial IoT device, using lightweight protocols sends messages to the cloud with roundtrip latency in the region of 300 milliseconds, when using “free-access” cloud servers. To unlock applications that will truly redefine industry 4.0 (computer vision, robotics or predictive maintenance in real time), there’s a need for AI enriched chips in IoT endpoints to catalyze data as it arrives.
As the size of data inputs from the edge increases, so does the strain on network infrastructure, which makes existing data highways to the cloud more fragile.
Machine learning at the edge will help assuage these pressures, but it must also cater for highly distributed infrastructures, containing hundreds or potentially thousands of sensors. Crucially, in many cases, AI in IoT will also be expected to disperse computing resources, switching between the connected edge and public or private cloud servers as needed.
Intelligence for All and Sundry
Intelligent edge chips now cover all ends of the spectrum. Capabilities vary from generalized and entry-level machine learning cores from market leader Arm, to digital signal processing units built for audio and video intelligence, or dedicated neural networks that pair to external microcontrollers.
As such, vendors are pushing architecture designs to their limits to find designs that not only provide immediate machine learning inference with limited energy consumption but also sufficient customization options for each enterprise’s specific requirements.
A gamut of integrated AI options exist to complement IoT microprocessors, targeting “smart tiny devices” for audio, voice and health monitoring, along with industrial machine vision, autonomous drones or intelligent surveillance cameras.
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