Swedish technology firm IAR Systems, which makes tools for software developers, has integrated ARM’s new machine learning technology into its B2B platform.
Developers using the IAR Embedded Workbench platform can now access ARM’s neural network kernels, it said.
Called Microcontroller Software Interface Standard (CMSIS), the system has been developed specifically for companies that use ARM Cortex-M processors.
The firm said the technology lets multicontroller developers simplify software reuse, reduce complex processes, and speed up the time it takes to bring new products to market.
CMSIS-NN is a powerful development tool that is presented as a library of neural network kernels. According to IAR, these can “maximise the performance and minimise the memory footprint of neural networks on ARM Cortex-M processor cores”.
Rise of edge tech
With the library, tech companies have an easier and quicker way to develop IoT edge devices. The use of neural networks is growing, in part due to their joint ability to improve efficiency and slash power consumption.
ARM’s machine learning technology is now a core part of AIR Embedded Workbench, which is described as a development toolchain for the Cortex-M series of microcontrollers.
Supporting more than 5,000 ARM devices, the system offers debugging and analysis tools for developers working on lower-power applications.
Machine versus man
Anders Lundgren, product manager of IAR Systems, said machine learning can help companies tap into the potential offered by IoT devices. “Neural networks and machine learning brings exciting new possibilities for embedded developers to move intelligent decisions down to the IoT devices,” he said.
“Developers making use of the powerful features of IAR Embedded Workbench and the ARM CMSIS-NN library will be able to use and maximise the power of embedded neural networks on microcontroller-based IoT edge devices.”
Tim Hartley, product manager of the machine learning group at ARM, said it has developed its latest machine learning technologies to give much-needed support to developers.
“ARM is committed to enabling industry-leading neural network frameworks and supporting leading toolchains, such as IAR Embedded Workbench, for optimising machine-learning applications on the smallest IoT edge devices,” he said.
“Deploying the CMSIS-NN libraries enables developers to achieve up to five times performance and efficiency improvements on Cortex-M processors for machine learning applications.”
Internet of Business says
The edge environment is emerging as a critical space in the development and spread of IoT devices and services, especially where embedded intelligence, modular and/or reusable technology, and lower power consumption are concerned.
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