To learn more about machine learning on Arm, see our range of available white papers:
Deploying Always-on Face Unlock: Integrating Face Identification, Anti-Spoofing, and Low-Power Wakeup
Accurate face verification has long been considered a challenge due to the number of variables, ranging from lighting to pose and facial expression. This white paper looks at a new approach – combining classic and modern machine learning (deep learning) techniques – that achieves 98.36% accuracy, running efficiently on Arm ML-optimized platforms, and addressing key security issues such as multi-user verification, as well as anti-spoofing.Read more
Packing Neural Networks into End-User Client Devices: How Number Representation Shrinks the Footprint
Most of today’s neural networks can only run on high-performance servers. There’s a big push to change this and simplify network processing to the point where the algorithms can run on end-user client devices. One approach is to eliminate complexity by replacing floating-point representation with fixed-point representation. We take a different approach, and recommend a mix of the two, so as to reduce memory and power requirements while retaining accuracy.Read more
The Power of Speech
Supporting Voice-Driven Commands in Small, Low-Power Microcontrollers.
Borrowing from an approach used for computer vision, we created a compact keyword spotting algorithm that supports voice-driven commands in edge devices that use a very small, low-power microcontroller.
The New Voice of the Embedded Intelligent Assistant
As intelligent assistance is becoming vital in our daily lives, the technology is taking a big leap forward. Recognition Technologies and Arm have published a white paper that provides technical insight into the architecture and design approach that’s making the gateway a more powerful, efficient place for voice recognition.