Why is NPU the key to enabling terminal side generative AI
NPU is designed specifically for Al and works together with other processors to accelerate the generative AI experience
The generative AI revolution has arrived. As the demand for generative AI use cases continues to increase in vertical fields with diverse and computational requirements, it is evident that we need a brand new computing architecture specifically designed for AI customization. This first requires a newly designed neural network processor (NPU) for generative AI, while utilizing heterogeneous processor combinations such as central processing units (CPUs) and graphics processing units (GPUs). By combining NPUs with appropriate processors, heterogeneous computing can achieve optimal application performance, energy efficiency, and battery life, empowering a new and enhanced generative AI experience.
Figure 1: Just like selecting the right tool in the toolbox, choosing the right processor depends on many factors, which will enhance the generative AI experience.
The Importance of Heterogeneous Computing
The diverse requirements and computational requirements of generative AI require different processors to meet. Supporting heterogeneous computing architectures that handle diversity can leverage the advantages of each processor, such as AI centric custom designed NPUs, as well as CPUs and GPUs. Each processor excels in different tasks: CPU excels in sequential control and immediacy, GPU is suitable for parallel data stream processing, NPU excels in scalar, vector, and tensor mathematical operations, and can be used for core AI workloads.
Heterogeneous computing can achieve optimal application performance, energy efficiency, and battery life to maximize the user experience of generative AI terminals.
The generative AI revolution has arrived. As the demand for generative AI use cases continues to increase in vertical fields with diverse and computational requirements, it is evident that we need a brand new computing architecture specifically designed for AI customization. This first requires a newly designed neural network processor (NPU) for generative AI, while utilizing heterogeneous processor combinations such as central processing units (CPUs) and graphics processing units (GPUs). By combining NPUs with appropriate processors, heterogeneous computing can achieve optimal application performance, energy efficiency, and battery life, empowering a new and enhanced generative AI experience.
Figure 1: Just like selecting the right tool in the toolbox, choosing the right processor depends on many factors, which will enhance the generative AI experience.
The Importance of Heterogeneous Computing
The diverse requirements and computational requirements of generative AI require different processors to meet. Supporting heterogeneous computing architectures that handle diversity can leverage the advantages of each processor, such as AI centric custom designed NPUs, as well as CPUs and GPUs. Each processor excels in different tasks: CPU excels in sequential control and immediacy, GPU is suitable for parallel data stream processing, NPU excels in scalar, vector, and tensor mathematical operations, and can be used for core AI workloads.
Heterogeneous computing can achieve optimal application performance, energy efficiency, and battery life to maximize the user experience of generative AI terminals.