Here on the Alif Semiconductor blog channel, we have been running a series called ‘Understanding the Ensemble difference’. It’s explained some of the unique features of the Ensemble microcontrollers and fusion processors, and the benefits that embedded product developers gain from them. Alif’s Balletto Bluetooth® wireless MCUs offer a similar set of benefits in edge AI MCU applications, enhancing performance and efficiency.
We’ve shown how you can make your system more secure with Ensemble or Balletto, get longer battery run-time, and produce AI inferencing results faster.
So the ‘Ensemble difference’ really matters when embedded device manufacturers set out to decide which product to base their next edge AI design on.
Additionally, choosing the right AI MCU can significantly influence the overall success of your embedded projects.
But is there such a thing as being too different for an AI MCU?
We are in the early days of implementing AI at the edge, and the market for edge AI processors has not yet settled into a stable, mature state. In an industry as dynamic and innovative as the semiconductor business, that means that competition is intense, and many companies with a wide variety of product concepts are emerging.
Some of these concepts appear wildly exotic. Various start-ups have come out with versions of ‘event-driven’ AI processors, for instance. Others refer to their devices for edge systems as ‘analog AI’ processors. These might be small, multi-function devices, but they are not microcontrollers. Many of them claim to provide a combination of fast AI performance and low power consumption by creating a kind of silicon architecture that mimics the physical structure of the human brain.
When Alif talks about the ‘Ensemble difference’, we do not mean anything quite as different as that. That’s a deliberate choice on our part, because there’s a big price to pay for too much difference.
Developers benefit from all the forms of standardization that have evolved in the embedded world. The microcontroller itself is a kind of standard: it’s the device that everyone knows, that provides the broad range of functions that most products need, and that is widely available at an affordable price.
Within this standard product category, there is a standard for the CPU: the Arm® Cortex®-M architecture. OEMs benefit from the thriving commercial market for tools that support the architecture, from the worldwide pool of design engineers who are familiar with the architecture, and of course from Arm’s long-term investment in developing the cores.
And now the edge AI world is moving further towards standardization, with the emergence of widely adopted machine learning frameworks such as Executorch – standardization means that users of the framework can expect to see their framework supported by standards-oriented providers such as Arm and MCU manufacturers such as Alif.
With their Arm Ethos™ neural processing units (NPUs) and Cortex-M55 CPUs, the Ensemble and Balletto MCUs are at the epicenter of this standards-based edge AI ecosystem. And while plenty of other MCU manufacturers also implement Arm cores and support popular ML frameworks, careful evaluation will show that standardization in MCUs does not mean that all MCUs are basically the same: in benchmark tests, other MCUs struggle to match the AI performance and efficiency that Alif MCUs offer – and that’s why it’s important to understand the Ensemble difference.