Virtual ARM

Virtual ARM (vARM) research encompasses diverse applications, primarily focused on optimizing performance and efficiency in various contexts. Current efforts concentrate on improving inference speed and reducing resource consumption for machine learning models on ARM architectures, often employing techniques like quantization and SIMD instruction sets, as well as developing novel algorithms for tasks such as best arm identification in bandit problems and efficient nearest neighbor search. These advancements are significant for deploying AI and machine learning on resource-constrained devices like those found in the Internet of Things (IoT) and mobile applications, leading to more efficient and powerful embedded systems.