Alif Semiconductor Achieves Increased Power & Performance for AI & ML Workloads on Arm Ethos-U55 microNPU and Cortex-M55 CPU Core

By Tiera Oliver

Associate Editor

Embedded Computing Design

October 14, 2021


Alif Semiconductor Achieves Increased Power & Performance for AI & ML Workloads on Arm Ethos-U55 microNPU and Cortex-M55 CPU Core

Alif's newly introduced Ensemble and Crescendo device families are designed to combine a dedicated high-performance and high-efficiency system to speed up AI/ML operations in embedded devices when compared to current CPU-bound approaches.

Alif Semiconductor, at Arm DevSummit on Oct 19 – 21 2021, will be demonstrating uplifts in Artificial Intelligence (AI) and Machine Learning (ML) workload performance and efficiency enabled by its Ensemble microcontroller (MCU), which include the Arm Ethos-U55 microNPUs and Arm Cortex-M55 CPU core.

Alif Semiconductors architecture is built around the concept of always-available, battery-friendly, environmental sensing. It's high-efficiency (HE) subsystem, powered by Arm Ethos-U55 microNPU and the Cortex-M55 CPU configured for low-power operations, can continuously monitor its surroundings for trigger events based on sound, vibration, images, and more with the help of AI-powered models. Once a trigger event is detected, a dedicated high-performance (HP) subsystem can examine and classify the event in detail and determine the correct action to take.

The HP subsystem contains another pair of Cortex-M55 and Ethos-U55, configured for additional performance and with additional memory to be able to run more complex decision and classification models. Both the HE and HP subsystems operate on top of Alif's Autonomous Intelligent Power Management (aiPM) fabric that is designed to ensure power is only consumed by portions of the device that need to be active.

The demonstration at Arm DevSummit will use the always-available HE subsystem on an Alif Ensemble MCU for keyword spotting (using DN-CNN model) to trigger the MCU's HP subsystem for image classification (using MobileNetV2 model). According to the company, the key results are:

  • High-Efficiency (HE) subsystem for keyword spotting
    • Per inference, Ethos-U55 + Cortex-M55 is 28.9x faster and 33.0x more energy efficient than Cortex-M55 alone
  • High-Performance (HP) subsystem for image classification
    • Per inference, Ethos-U55 + Cortex-M55 is 75.2x faster and 76.0x more energy efficient than Cortex-M55 alone
  • As claimed by Arm, when both Ethos-U55 and Cortex-M55 are used together there is an 800x faster time-to-inference than compared to a MCU using a previous generation Cortex-M CPU with no microNPU.

Using Arm technology, Alif is enabling always-sensing battery-powered AI/ML capabilities on the edge in a way that, according to the company, previously was not possible. This can unlock the true potential for machine learning in embedded applications.

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Tiera Oliver, Associate Editor for Embedded Computing Design, is responsible for web content edits, product news, and constructing stories. She also assists with newsletter updates as well as contributing and editing content for ECD podcasts and the ECD YouTube channel. Before working at ECD, Tiera graduated from Northern Arizona University where she received her B.S. in journalism and political science and worked as a news reporter for the university’s student led newspaper, The Lumberjack.

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