ARM recently announced Project Trillium, a suite of ARM intellectual property including new scalable processors that will deliver enhanced machine learning and neural network functionality.
ARM recently announced Project Trillium, a suite of ARM intellectual property including new scalable processors that will deliver enhanced machine learning and neural network functionality.
Project Trillium will bring machine learning to edge devices. Along with object detection processors, better user experience may be brought to broader range of applications. In addition, the new products will enable trillions of machine learning operations per second on mobile devices.
The rapid acceleration of artificial intelligence into edge devices is placing increased requirements for innovation to address compute while maintaining a power efficient footprint. To meet this demand, Arm is announcing its new machine learning platform Project Trillium, the company said.
New devices will require the high-performance machine learning and AI capabilities “Combined with the high degree of flexibility and scalability that our platform provides, our partners can push the boundaries of what will be possible across a broad range of devices,” said Rene Haas, President of IP Products Group at ARM.
Machine learning technologies today tend to focus on specific device classes or the needs of individual sectors. ARM’s Project Trillium changes that by offering more scalability. While the initial launch focuses on mobile processors, future Arm machine learning products will deliver the ability to move up or down the performance curve – from sensors and smart speakers, to mobile, home entertainment, and more, the company said.
The ARM object detection processor has been designed specifically to identify people and other objects with virtually unlimited objects per frame, featuring real-time detection with Full HD processing at 60 frames per second, up to 80x the performance of a traditional DSP, and a significant improvement in detection quality relative to previous ARM technologies.
Users will enjoy high-resolution, real-time, detailed face recognition on their smart devices delivered in a battery-friendly way, ARM said.