We invest in semiconductor design business especially for AI and its complex SoC design related micro electronics companies.

Semiconductor design is crucial for AI for several reasons:

1. Performance: AI models, especially deep learning networks, require massive computational power. Advanced semiconductor designs enable the development of processors like GPUs and TPUs that can handle parallel processing efficiently, leading to faster training and inference times.

2. Energy Efficiency: AI workloads can be very demanding in terms of energy consumption. Optimized semiconductor designs help create chips that perform more computations per watt, which is essential for scaling AI applications, especially in mobile and edge devices.

3. Specialized Architectures: Custom semiconductor designs allow for specialized architectures tailored for specific AI tasks. For example, neural processing units (NPUs) and dedicated hardware for specific algorithms can significantly enhance performance.

4. Scalability: As AI applications grow in complexity and size, scalable semiconductor solutions are needed. Well-designed chips can support larger datasets and more complex models without a linear increase in power consumption or cost.

5. Cost-Effectiveness: Innovative semiconductor designs can reduce the overall cost of AI systems by optimizing performance and efficiency, making AI more accessible to a wider range of applications and industries.

6. Integration: Advanced semiconductor design allows for better integration of various components (like memory, processing units, and communication interfaces) on a single chip, reducing latency and improving overall system performance.