The Alif Blog

Getting Started with Machine Learning on Alif Semiconductor Microcontrollers

If you’re working on embedded applications, there’s a good chance that machine learning (ML) is on your radar. For many venturing into this world, gathering the appropriate hardware and software resources necessary to begin your development journey is a big challenge. Where edge AI/ML can sometimes feel esoteric and daunting, a good development platform is necessary to get started in the field.

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Bringing Transformer Networks to the Edge with Arm Ethos-U85

Introduced by Google in 2017, Transformer models have been a revolutionary concept in machine learning. Offering a paradigm shift in sequence transduction, this novel neural network architecture has significantly enhanced the efficiency and accuracy of various AI applications, enabling breakthroughs in tasks such as natural language processing, speech recognition, and real-time analytics.

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Alif Semiconductor Packs Class-Leading Endpoint Machine Learning Into Tiny Footprint with E1C

Alif also scales “down” to address markets that demand the smallest form factors and the lowest power consumption. To this end, Alif has just introduced the Ensemble E1C series. This new member of the Ensemble family combines a 160MHz Arm Cortex-M55 CPU core with Helium vector processing extension, an Arm Ethos-U55 NPU performing at up to 46 giga-operations-per-second (GOPs), and up to 2MB of tightly coupled SRAM, all presented in tiny packages as small as 3.9mm x 3.9mm.

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MCUs with integrated NPU Cores: Making Edge AI a Reality

The field of edge computing is one of the fastest-growing sectors of the computing industry.  Recent data indicates that the global edge computing market was valued at $16.45 billion in 2023, with expectations to grow at a CAGR of 36.9% through 2030. With such growth on the horizon, it is no surprise that the rising demand for artificial intelligence (AI) at the edge continues to completely change the way the industry is approaching computing.

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