AI Accelerators MCQs

By: Prof. Dr. Fazal Rehman | Last updated: September 20, 2024

What is the primary purpose of AI accelerators? A) To slow down processing speeds B) To enhance the performance of AI applications C) To replace traditional CPUs entirely D) To increase power consumption Answer: B) To enhance the performance of AI applications Which of the following is an example of an AI accelerator? A) General-purpose CPU B) Graphics Processing Unit (GPU) C) Hard Disk Drive (HDD) D) Random Access Memory (RAM) Answer: B) Graphics Processing Unit (GPU) What distinguishes an AI accelerator from a traditional processor? A) Higher power consumption B) Specialized architecture for parallel processing C) Limited processing capabilities D) Exclusively used for gaming Answer: B) Specialized architecture for parallel processing What type of workload is an AI accelerator best suited for? A) Simple calculations B) Complex machine learning tasks C) Basic data storage D) Static webpage rendering Answer: B) Complex machine learning tasks Which of the following technologies is often integrated with AI accelerators? A) Cloud computing B) Mainframe computing C) Virtual Reality (VR) D) Traditional databases Answer: A) Cloud computing What is the main benefit of using Tensor Processing Units (TPUs)? A) Enhanced performance for video rendering B) Optimized for neural network computations C) Greater storage capacity D) Decreased processing speeds Answer: B) Optimized for neural network computations How do FPGAs (Field-Programmable Gate Arrays) function as AI accelerators? A) They are fixed-function devices B) They can be reprogrammed to optimize specific tasks C) They only support general-purpose applications D) They increase data retrieval times Answer: B) They can be reprogrammed to optimize specific tasks What is a key feature of dedicated AI chips? A) They can only run one application at a time B) They are designed for specific AI workloads C) They operate slower than general-purpose CPUs D) They require manual configuration for every task Answer: B) They are designed for specific AI workloads In which area are AI accelerators predominantly used? A) Web browsing B) Data analysis C) Gaming D) Image and speech recognition Answer: D) Image and speech recognition What advantage do AI accelerators provide for deep learning models? A) Decreased model accuracy B) Faster training and inference times C) Higher energy consumption D) Increased model complexity Answer: B) Faster training and inference times Which type of neural network benefits the most from GPU acceleration? A) Recurrent Neural Networks (RNNs) B) Convolutional Neural Networks (CNNs) C) Linear regression models D) Decision trees Answer: B) Convolutional Neural Networks (CNNs) What is a common challenge when using AI accelerators? A) Limited compatibility with modern software B) Higher initial cost compared to traditional CPUs C) Decreased processing capabilities D) Inability to perform complex computations Answer: B) Higher initial cost compared to traditional CPUs Which of the following applications can benefit from AI accelerators? A) Office applications B) Autonomous vehicles C) Static webpage creation D) Simple text editing Answer: B) Autonomous vehicles What is the role of hardware accelerators in AI inference? A) To slow down the processing of AI models B) To improve the speed and efficiency of model execution C) To eliminate the need for software D) To increase power consumption Answer: B) To improve the speed and efficiency of model execution How do AI accelerators impact energy efficiency? A) They increase energy usage significantly B) They allow for faster processing with lower energy consumption C) They have no effect on energy efficiency D) They only benefit large-scale data centers Answer: B) They allow for faster processing with lower energy consumption Which technology allows for parallel processing in AI applications? A) Central Processing Unit (CPU) B) Field-Programmable Gate Array (FPGA) C) Solid-State Drive (SSD) D) Optical drives Answer: B) Field-Programmable Gate Array (FPGA) What is the main advantage of using ASICs (Application-Specific Integrated Circuits) for AI? A) General-purpose usability B) High performance for specific tasks C) Increased programming complexity D) Limited power efficiency Answer: B) High performance for specific tasks Which of the following best describes the role of AI accelerators in natural language processing (NLP)? A) They are not applicable in NLP B) They help in speeding up model training and inference C) They reduce the complexity of language models D) They eliminate the need for data preprocessing Answer: B) They help in speeding up model training and inference What is a significant trend in the development of AI accelerators? A) Moving away from specialized hardware B) Increased integration with general-purpose processors C) Focus on smaller, more efficient designs D) Decreasing interest in parallel processing Answer: C) Focus on smaller, more efficient designs Which of the following is a potential application of AI accelerators in healthcare? A) Traditional patient record management B) Real-time diagnostic imaging analysis C) Manual data entry D) Basic report generation Answer: B) Real-time diagnostic imaging analysis How do AI accelerators affect the deployment of AI in edge computing? A) They limit the capabilities of edge devices B) They enable real-time processing at the edge C) They require constant cloud connectivity D) They decrease the need for local processing Answer: B) They enable real-time processing at the edge Which of the following is a benefit of using GPUs for AI workloads? A) Slower processing times B) High throughput for parallel tasks C) Limited memory capacity D) Increased latency Answer: B) High throughput for parallel tasks What is the primary function of a neural processing unit (NPU)? A) To handle general computing tasks B) To accelerate artificial intelligence computations C) To manage cloud storage D) To support legacy applications Answer: B) To accelerate artificial intelligence computations What is a potential drawback of using AI accelerators in production environments? A) Enhanced processing capabilities B) Dependence on specialized software C) Decreased training times D) Improved scalability Answer: B) Dependence on specialized software Which of the following is NOT a feature of modern AI accelerators? A) Enhanced processing power B) Support for mixed precision computation C) Limited interoperability D) High energy efficiency Answer: C) Limited interoperability In what scenario are AI accelerators least beneficial? A) Real-time data analysis B) Running simple, single-threaded applications C) Complex simulations D) Image processing tasks Answer: B) Running simple, single-threaded applications How can AI accelerators contribute to the development of autonomous systems? A) By slowing down data processing B) By enabling faster decision-making through local processing C) By limiting the use of sensors D) By increasing reliance on human intervention Answer: B) By enabling faster decision-making through local processing Which of the following describes the relationship between AI accelerators and machine learning frameworks? A) They operate independently of each other B) AI accelerators are designed to optimize performance of specific frameworks C) Machine learning frameworks eliminate the need for accelerators D) They are only relevant for traditional programming languages Answer: B) AI accelerators are designed to optimize performance of specific frameworks What is one of the main reasons companies invest in AI accelerators? A) To increase operational costs B) To remain competitive in AI innovation C) To reduce processing capabilities D) To limit their data processing speed Answer: B) To remain competitive in AI innovation Which type of processing task can benefit most from the parallel architecture of AI accelerators? A) Sequential data processing B) Batch processing C) Simple arithmetic calculations D) Real-time decision-making Answer: D) Real-time decision-making How do AI accelerators influence the future of AI research? A) By restricting research capabilities B) By enabling faster experimentation and innovation C) By focusing solely on traditional methods D) By decreasing the demand for AI applications Answer: B) By enabling faster experimentation and innovation What is a common feature of edge AI devices that utilize AI accelerators? A) Large physical size B) Limited power efficiency C) Real-time data processing capabilities D) Exclusive reliance on cloud resources Answer: C) Real-time data processing capabilities

Read More Computer Architecture MCQs

1. SET 1: Computer Architecture MCQs
2. SET 2: Computer Architecture MCQs
3. SET 3: Computer Architecture MCQs
4. SET 4: Computer Architecture MCQs
5. SET 5: Computer Architecture MCQs
6. SET 6: Computer Architecture MCQs
7. SET 7: Computer Architecture MCQs
8. SET 8: Computer Architecture MCQs
9. SET 9: Computer Architecture MCQs

• Introduction to Computer Architecture MCQs
  • Evolution of Computer Architecture MCQs
  • von Neumann vs Harvard Architecture MCQs
• Basic Components of a Computer System MCQs
  • Central Processing Unit (CPU) MCQs
  • Memory (RAM, ROM, Cache) MCQs
  • Input/Output (I/O) Devices MCQs
  • Buses (Data, Address, Control) MCQs
• CPU Organization MCQs
  • ALU (Arithmetic Logic Unit) MCQs
  • Control Unit MCQs
  • Registers (General Purpose, Special Purpose) MCQs
  • Flags and Status Registers MCQs
• Instruction Set Architecture (ISA) MCQs
  • Types of Instructions (Data Movement, Arithmetic, Control) MCQs
  • Addressing Modes (Immediate, Direct, Indirect, Indexed) MCQs
  • Instruction Formats MCQs
  • RISC (Reduced Instruction Set Computer) vs CISC (Complex Instruction Set Computer) MCQs
• Microarchitecture MCQs
  • Pipelining (Stages and Hazards) MCQs
  • Superscalar Architecture MCQs
  • Multithreading MCQs
  • Out-of-Order Execution MCQs
• Memory Hierarchy MCQs
  • Cache Memory (L1, L2, L3) MCQs
  • Virtual Memory MCQs
  • Paging and Segmentation MCQs
  • TLB (Translation Lookaside Buffer) MCQs
• Cache Memory MCQs
  • Cache Mapping Techniques (Direct, Associative, Set-Associative) MCQs
  • Cache Coherency MCQs
  • Write Policies (Write-Through, Write-Back) MCQs
  • Cache Miss and Hit MCQs
• Input/Output Organization MCQs
  • I/O Techniques (Polling, Interrupts, DMA) MCQs
  • I/O Mapping (Memory-Mapped I/O, Isolated I/O) MCQs
  • Interrupt Handling MCQs
• Bus Architecture MCQs
  • System Bus (Data, Address, Control) MCQs
  • Bus Arbitration MCQs
  • Synchronous vs Asynchronous Buses MCQs
• Performance Metrics MCQs
  • Clock Speed and Cycles per Instruction (CPI) MCQs
  • MIPS (Million Instructions per Second) MCQs
  • FLOPS (Floating-Point Operations per Second) MCQs
  • Amdahl’s Law MCQs
• Parallelism in Computer Architecture MCQs
  • Instruction-Level Parallelism (ILP) MCQs
  • Data-Level Parallelism (DLP) MCQs
  • Thread-Level Parallelism (TLP) MCQs
  • Symmetric Multiprocessing (SMP) vs Asymmetric Multiprocessing (AMP) MCQs
• Multicore and Multiprocessor Systems MCQs
  • Multicore Architectures MCQs
  • Shared Memory vs Distributed Memory MCQs
  • Interconnection Networks MCQs
• Control Unit Design MCQs
  • Hardwired Control vs Microprogrammed Control MCQs
  • Control Signals and Timing MCQs
  • Instruction Cycle and Control Flow MCQs
• Pipeline Hazards MCQs
  • Structural, Data, and Control Hazards MCQs
  • Techniques to Overcome Hazards (Stalling, Forwarding, Branch Prediction) MCQs
• Branch Prediction and Speculation MCQs
  • Static and Dynamic Branch Prediction MCQs
  • Speculative Execution MCQs
  • Branch Target Buffers MCQs
• Arithmetic and Logic Operations MCQs
  • Integer and Floating-Point Arithmetic MCQs
  • ALU Design MCQs
  • Fixed-Point vs Floating-Point Representation MCQs
• Memory Management MCQs
  • Paging, Segmentation, and Virtual Memory MCQs
  • Memory Protection and Privilege Levels MCQs
  • Memory Allocation and Deallocation MCQs
• Power and Energy Efficiency MCQs
  • Power Dissipation in CPUs MCQs
  • Low-Power Design Techniques MCQs
  • Energy-Aware Computing MCQs
• Advanced Topics MCQs
  • GPU Architecture and Usage MCQs
  • Quantum Computing Basics MCQs
  • Neuromorphic Computing MCQs
• Emerging Trends
  • Cloud Computing Architectures MCQs
  • Edge Computing MCQs
  • AI Accelerators MCQs