The amount of computing resources dedicated to running AI algorithms doubles every 100 days, as the demand for AI-powered applications soars. Use cases today may look very different 5 years from now, yet designers need to plan for the unknown as they build their products.
Scaling to meet future ML needs can be challenging as many AI compute solutions currently are fragmented, with incompatible hardware that may not scale. This blog discusses how the path to scaling AI lies in trusted, standard, and complete platforms that address both diverse hardware and software requirements.
With Project Trillium, Arm delivers the versatile hardware IP and open-source software tools and ecosystem partnerships that enable developers to design and scale their AI and machine learning applications quickly, securely, and efficiently.
Arm's ML-optimized hardware runs ML workloads with the highest performance and most efficiency - with processors that handle ML algorithms in more than 85 percent of mobile devices all the way through to ML on the smallest IoT endpoint devices. Offering the flexibility to choose from a range of CPUs, graphic processors (GPUs), and neural processors (NPUs), Arm has the IP to fit the use case.
Based on the concept of develop once, deploy everywhere, Arm’s flexible software framework - Arm NN, Compute Library, and CMSIS-NN - supports workloads across all programmable Arm IP, and can extend to cover new features in existing IP, and new core types.
Choosing the Right Processor IP for your Machine Learning Application
This must-read guide explores key considerations when choosing the right processor IP mix for machine learning, ensuring an optimal balance of ML system performance, cost, and product design.
Arm’s AI global ecosystem includes cutting-edge software companies that are enabling the next generation of intelligent edge devices.