The need for AI on-the-go is a main driver for the creation of AI mobile applications like chatbots, real-time translation, and image/video generation. In some cases these applications use models that must reside on cloud servers due to their hardware and high accuracy requirements. SMEs are at a disadvantage with cloud-based AI because of their limited budgets. Meanwhile, smartphones are handling AI tasks better due to hardware improvements in chips like the Apple A17 Pro, Google Tensor G3 and the Qualcomm Snapdragon 8 Gen 3. These System-on-Chips (SoCs) will drive edge AI on smartphones–allowing large enterprises to leverage this technology.
AI teams can use model quantization to help SMEs to keep pace with the big players. Model quantization adds edge AI to mobile applications and improves inference speed, reduces cost and reduces hardware requirements. Model quantization also …