Nuvoton and Skymizer Achieve MLPerf Tiny Benchmark Leadership Using NuMaker M467HJ

Nuvoton M467-series MCU uses Arm Cortex-M4F microcontroller at 200MHz, 67% faster than typical, achieving class-leading inference performance through ML optimization using Skymizer's neural network technologies.

A Highly Capable Platform for Embedded Solutions Using Edge AI
MLCommons is an independent machine learning performance benchmarking association, which has become the trusted standard for evaluating ML performance on a wide range of systems. The MLPerf Tiny Benchmark focuses on practical ML use cases that run on embedded systems, including visual wake word, keyword spotting, image classification, and audio anomaly detection.

The Nuvoton M467 series boasts a large 512KB of SRAM, 1024KB of Flash memory, and a rich set of integrated system features and peripherals, including DSP, FPU, DMA, CAN-FD, I2S, USB, camera interface, crypto accelerators, and 10/100 Ethernet MAC. These features make it an ideal choice for systems designers to build devices that integrate tinyML into various emerging applications, such as smart home automation, smart city and infrastructure, light edge AI in IoT, smart manufacturing, and many more emerging use cases.

Skymizer's ONNC compiler played a crucial role in optimizing the machine learning software stack for the M467-series Cortex-M4F, resulting in remarkable gains in inference speed and enhancing the overall performance of machine learning applications.

The collaboration between Nuvoton Technology Corporation and Skymizer showcases their commitment to providing cutting-edge solutions for ML in power-efficient embedded systems. By combining hardware excellence with software optimization, they have unlocked new possibilities for ML-based applications on MCU, enhancing capabilities across industries.

The outstanding results achieved in the MLPerf Tiny Benchmark's Cortex-M4 MCU segment highlight Nuvoton's and Skymizer's dedication to pushing the boundaries of machine learning performance in resource-constrained environments.

Learn more about the Nuvoton NuMaker M467HJ evaluation board: https://www.nuvoton.com/board/numaker-m467hj/.

See the MLPerf Tiny Benchmark result here: https://mlcommons.org/en/inference-tiny-11/.

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