• Second Benchmark testing for the ECS-DOT chipset by Embedded A.I Systems Pte. Ltd (EMASS) is scheduled to commence on 7 November, 2024, with results expected within a week
• The tests will focus on two critical applications, Anomaly Detection and Keyword Spotting, using standardised workloads from the MLPerf Tiny AI suite
• The EMASS ECS-DOT’s chipset performance will be compared against current System-on-a-Chip semiconductor leaders, STMicroelectronics and Syntiant
• Benchmarking results will be released to the market upon completion
• These tests provides an accelerated framework for commercialisation using the EMASS System on Chip technologies.

Nanoveu (ASX: NVU) has commenced the next phase in benchmark testing for the ECS-DOT chipset, developed by Embedded A.I Systems (EMASS). This step reinforces the Company’s strategy to leverage EMASS’s edge AI semiconductor chip technology and demonstrate its potential for industry-leading AI performance and energy consumption efficiency.

The benchmarking process will continue to evaluate the ECS-DOT System-on-a-Chip (SoC) using MLPerf Tiny benchmarking suite, an industry-standard suite providing reproducible, reliable assessments of SoC performance in speed, accuracy, and energy efficiency. The focus will be on two key applications: (1) Anomaly Detection and (2) Keyword Spotting. Both these tasks are crucial for real-time AI applications in fields ranging from industrial IoT and consumer electronics to 2D-3D visual data transformation.

Commenting on the benchmarking, Alfred Chong, Managing Director and CEO of Nanoveu said: “As we continue to expand the EyeFly3D™ platform’s reach, these benchmarking tests are a pivotal step in preparing for EMASS chipset integration. This phase enables us to validate the performance of ECS-DOT chipset in powering next-generation 3D experiences. By demonstrating the chipset’s strengths in tasks like anomaly detection and keyword spotting, we’re positioning EMASS to meet the growing demand for powerful, energy-efficient SoCs with advanced image processing capabilities across industries like smart home systems, robotics, autonomous driving, and medical imaging.”