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The Alif Blog

A vision of the future: MCU-based edge AI devices to take center stage in consumers’ digital lives

AI offers the biggest opportunity for many endpoint or edge IoT device manufacturers to add value to their products and achieve meaningful differentiation. But because of edge AI devices’ limited resources – power, processor bandwidth and size – manufacturers are continually bumping up against the ceiling of what they can do in AI with a microcontroller.

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Understanding the Ensemble difference: MCU’s granular power management circuit produces extreme power savings

We have explained previously how Ensemble and Balletto microcontrollers pair a neural processing unit (NPU) with a CPU to produce very fast AI inferences in edge applications, while also performing system control functions as a conventional MCU does. At the edge, a small battery is the most common power source. Is this compatible with AI?

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Understanding the Ensemble difference: Built-in secure enclave provides strong cyber-protection to edge AI devices

Few manufacturers of connected embedded devices need to be reminded that their products are exposed to the risk of cyber-attack. This security threat is often presented in terms of the danger that the device’s systems will be damaged or disabled, or the risk to the data that the device stores or transmits. But when an embedded device includes edge AI capability, the biggest security priority might become protection for the IP embedded in the manufacturer’s AI models.

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Understanding the Ensemble Difference: Intelligent partitioning extends battery run-time in edge AI devices

Traditionally, microcontrollers’ architecture has been built for old-style system control workloads. The model for the MCU’s operation was the execution of a sequence of tasks: the MCU would fire up its CPU to process the instructions required to fulfil the tasks as fast as possible, and then retire the CPU to a low-power quiescent state until the next set of tasks presented itself.

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