Realize the function of intelligent edge side

DDC4-10-40020 We can see that many of Arm’s partners are introducing Helium technology into their latest products to help developers take advantage of ML capabilities on restricted devices at the farthest end of the network. In February 2020, Arm introduced the Helium Cortex-M55 processor, and Alif Semiconductor introduced its first Cortex-M55 based chip in September 2021. It also deployed the Helium powered Cortex-M55 processor in its Ensemble and Crescendo product lines. In addition, Himax has also adopted the Helium equipped Cortex-M55 in its next generation WE2 AI processor, targeting computer vision systems in battery-powered iot devices.

In April 2022, Arm launched its second Helium capable CPU, the Arm Cortex-M85. DDC4-10-40020 Renesas has demonstrated the Cortex-M85 at embedded world 2022 and embedded world 2023. In the demonstration, Plumerai significantly sped up its inference engine with Renesas Electronics RA MCU technology. As a company that develops complete software solutions for camera-based person detection, Plumerai believes the performance improvements will ensure its customers can take advantage of the larger, more accurate version of Plumerai’s person detection AI, while providing more product features and extended battery life. In November 2023, Arm introduced its third Helium CPU, the Cortex-M52, a processor designed for artificial Intelligence Internet of Things (AIoT) applications that can deliver significant performance gains for DSP and ML applications in small, low-power embedded devices. Deploy more compute-intensive ML inference algorithms in endpoints without the need for a dedicated NPU.

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As hardware evolves, developers face increasing software complexity, requiring new development processes to create optimized ML models that incorporate efficient device drivers. It is critical that the software development platforms and tools for the ecosystem also evolve with the hardware.

DDC4-10-40020 A variety of tools are available today from Arm and third parties to support end users in creating AI algorithms. Once a data scientist has created a model in an offline environment, they can use the appropriate tools to optimize the model to run on an Arm EthO ™-U based NPU or use the Helium instruction on a Cortex-M based processor.

Qeexo is the first company to implement end-to-end ML automation for edge devices, with its AutoML platform providing an intuitive user interface (UI) that allows users to collect, clean, and visually present sensor data, and use different algorithms to automatically DDC4-10-40020 build ML models. Traditional embedded tools such as the Keil Microcontroller Development Kit (Keil MDK) are a useful complement to MLOps tools and help establish DevOps processes for validating complex software workloads. As a result, embedded, iot, and AI applications are finally converging in a single development process that software developers are familiar with.

The potential at the edge is being tapped. The need to improve the performance of microcontrollers continues to grow, especially for tasks such as voice-activated door locks, person detection and recognition, networked motor control with predictive maintenance, and countless other high-end AI and ML applications.

We believe that with the right technology, developers can reimagine edge and end-side devices and strike the right balance between the key elements of constrained devices such as performance, cost, power efficiency and privacy, enabling future embedded development to implement AI computing.