1.1.2 Types of processors

• What does CISC stand for, and what is its primary characteristic?

  CISC stands for Complex Instruction Set Computing. Its primary characteristic is having a large set of instructions that aim to accomplish tasks with fewer lines of assembly code, focusing on reducing the number of instructions per program.
• What does RISC stand for, and how does it differ from CISC?

  RISC stands for Reduced Instruction Set Computing. Unlike CISC, RISC focuses on a smaller set of instructions that are highly optimized for efficiency, aiming to execute instructions at a faster rate by using a larger number of registers.
• What are typical uses of CISC and RISC processors?

  CISC processors are commonly used in general-purpose computers where varied and complex tasks are performed. RISC processors are often found in mobile devices and embedded systems where power efficiency and performance per watt are crucial.
• What is a GPU, and what are its primary uses?

  A GPU, or Graphics Processing Unit, is a specialized processor designed for accelerating graphics rendering. Its primary uses include video rendering, gaming graphics, and in recent times, parallel processing tasks like machine learning and data analysis.
• What defines a multicore processor?

  A multicore processor contains two or more processing units (cores) within a single chip. Each core can execute program instructions independently, enhancing performance and allowing for more efficient multitasking.
• What is the advantage of parallel processing systems?

  Parallel processing systems can run multiple processes simultaneously, significantly speeding up computing tasks and improving efficiency in tasks that can be divided into smaller, concurrent operations.
• Why are RISC processors preferred in mobile devices?

  RISC processors are preferred in mobile devices because their simplified instructions set can be executed quickly and efficiently, leading to better battery life and performance, which is essential for mobile computing.
• Why are GPUs important in AI and Machine Learning?

  GPUs are crucial in AI and Machine Learning because they can handle thousands of threads simultaneously, making them exceptionally good at performing the large scale, parallel computations required in training and running neural networks.