This report discusses the leading players in the Edge AI and AIoT chipset space. The rapid growth of AIoT devices with diverse processing needs along with the proliferation of innovative chip designs is leading to the emergence of optimised edge AI and AIoT chipsets, used across various applications like natural language processing, computer vision, anomaly detection, and predictive maintenance. Such machine learning algorithms on edge devices are often required to operate at much lower levels of power consumption than equivalent algorithms running on servers or in data centres, due to limitations of availability of power.
Increasingly vendors are offering edge AI chipsets with various value-added capabilities to differentiate their offerings. In light of this, several AIoT chipset providers have begun offering end-to-end solutions by providing software suites of tools. These software solutions enable businesses to build, test, and deploy customised machine learning (ML) applications by facilitating pre-silicon ML workload simulations and evaluating key performance metrics for their chips and more.
Typically, such software suites consist of APIs, development tools, and prebuilt software libraries, supporting developers to deploy ML models on edge AI chips. They often incorporate features like quantisation and compression tools to optimise large language and other models to fit within the constraints of silicon with limited memory and computational capacity, aligning the models with the chip's architecture. Also, chipset providers are looking for new ways to widen and expand their revenue opportunities and increase their chipset adoption across industries by offering software tools. Thus, some companies are strengthening their software capabilities to offer a deep learning software development kit, ensuring a seamless development, deployment, and testing of edge machine learning applications.
