Alif Semiconductor demonstrates a 76x uplift in power efficiency
Also delivers increase in inference performance for edge node AI/ML workloads using Arm's new Ethos-U55 microNPU and Cortex-M55 CPU core
At this week’s Arm DevSummit, Alif Semiconductor will be demonstrating dramatic 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.
Users of traditional general purpose MCUs want access to AI/ML technology in embedded applications to improve existing solutions and be able to build elegant new solutions to problems that traditional methods in the embedded space cannot solve. So far, AI/ML techniques have not been a good fit for embedded applications, as they have been designed without embedded constraints such as available memory, adequate CPU performance, network access, and low power consumption in mind.
Alif meets this need with its newly introduced Ensemble and Crescendo device families, that combine a dedicated high-performance and high-efficiency system to efficiently speed up AI/ML operations in embedded devices at a significant level, when compared to current CPU-bound approaches.
“Enabling AI everywhere requires device makers and developers to deliver machine learning locally in embedded applications on billions of devices,” said Dennis Laudick, vice president commercial and marketing, Machine Learning Group at Arm. “With first in-silicon instance of the combined Ethos-U55 and Cortex-M55 solution, Alif is demonstrating the significant performance and efficiency gains that are being made possible by Arm ML technology, unleashing the potential of AI securely across a vast range of life-changing applications.”
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 specifically 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 rapidly 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 ensures power is only consumed by portions of the device that need to be active, extending battery life even further.
“One of the key drivers behind the founding of Alif has been to close the gap in the market for embedded IoT devices that are able to perform Machine Learning workloads in a power-efficient manner,” said Reza Kazerounian, president & co-founder, Alif Semiconductor. “By combining our innovative power management, and our highly scalable and performance-oriented architecture with Arm Cortex-M55 & Ethos-U55, we have created the perfect platform for delivering efficient edge processing to tomorrows IoT devices.”