With the ever-increasing demand for high-performance computing and the emergence of new computing paradigms like neuromorphic computing and quantum computing, how can computer engineers design and develop future computer systems that can efficiently handle complex workloads like artificial intelligence, big data analytics, and scientific simulations, while considering challenges such as:

• Heterogeneous computing architectures:  How can computer engineers design systems that combine different processing units (CPUs, GPUs, specialized accelerators) to achieve optimal performance for diverse computing tasks?
• Power efficiency and thermal management: How can new computer systems be designed to minimize power consumption and heat generation, especially with the growing density and complexity of processing units?
• Scalability and manageability: How can future computer systems be designed to scale efficiently to accommodate ever-growing datasets and increasingly complex workloads, while also ensuring ease of management and programmability?
• Security vulnerabilities of emerging architectures: How can potential security vulnerabilities inherent in new computing paradigms like neuromorphic and quantum computing be mitigated to ensure the integrity and confidentiality of sensitive data?
• Programming models for new architectures:  How can new programming models and software development tools be developed to effectively utilize the unique capabilities of emerging computing paradigms and bridge the gap between theoretical concepts and practical implementation?