Artificial intelligence (AI) technologies have gained significant attention and interest within the knowledge work industry. Research by TechTarget’s Enterprise Strategy Group (ESG) reveals that organizations are actively adopting AI strategies to transform their businesses, IT environments, and teams.
The rise of AI at the edge is driven by several factors. As everything becomes interconnected and more complex, the edge has emerged as a new frontier. This complexity increases the challenges of data transmission and management. GenAI systems like ChatGPT have captured the imagination of the technology industry and are crucial for real-time performance, data security, and customization in applications such as autonomous driving and robotics.
Edge AI is being initially deployed in key areas such as healthcare, smart retail, communications, smart cities, automotive, digital home, and intelligent factories. In healthcare, AI is revolutionizing convenience and quality of life by helping doctors make accurate diagnoses. In automotive, AI powers driver-assist and advanced safety features, enabling personalization and convenience. Industrial applications rely on AI for robotics, vision systems, and efficient information availability.
However, there are technical challenges to overcome in edge AI. The rapid pace of AI innovation puts stress on computing capabilities. An adaptable infrastructure is needed to support AI strategies, including CPUs, GPUs, and PLCs. Edge AI also faces challenges such as power, data latency, accuracy, safety, security, and diverse workloads.
Each use case presents specific challenges. Industrial applications require coping with diverse workloads, regulation, safety, and accuracy. Healthcare demands accuracy, safety, security, power, and data accuracy. Automotive applications focus on latency, accuracy, safety, and regulation. Integration of multiple systems is essential to designing edge AI applications that support scalability and adaptability.
Leading innovation in edge AI is driven by processor and platform giants like Qualcomm Technologies, Intel, MediaTek, AMD, ARM, Advantech, Adlink, Cadence Design Systems, Microsoft, and Bosch. Partnerships between stakeholders are crucial to building an edge AI ecosystem. For example, Qualcomm’s collaboration with Advantech aims to establish an open and diverse edge AI ecosystem for AIoT applications. ARM has introduced the Ethos-U85 NPU to support edge AI, emphasizing consistent toolchains and partnerships for a seamless developer experience.
In conclusion, AI technologies at the edge present significant opportunities and challenges within various industries. Organizations are harnessing the power of AI to transform their operations and improve efficiency, but they must overcome technical hurdles and build strong partnerships to fully realize the potential of edge AI.