Neuromorphic Computing MCQs

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

What is neuromorphic computing primarily inspired by?
A) Traditional von Neumann architecture
B) Biological neural networks
C) Quantum computing principles
D) Classical logic circuits
Answer: B) Biological neural networks

Which of the following describes a key feature of neuromorphic systems?
A) Use of binary logic
B) Parallel processing capabilities
C) Sequential processing
D) High power consumption
Answer: B) Parallel processing capabilities

What type of architecture does neuromorphic computing utilize?
A) Linear architecture
B) Event-driven architecture
C) Hierarchical architecture
D) Centralized architecture
Answer: B) Event-driven architecture

Which of the following is a primary advantage of neuromorphic computing?
A) Increased complexity of algorithms
B) Energy efficiency and low power consumption
C) Exclusive focus on numerical calculations
D) Dependence on cloud computing
Answer: B) Energy efficiency and low power consumption

What is the function of spiking neural networks (SNNs) in neuromorphic computing?
A) To execute binary operations
B) To model the way biological neurons communicate
C) To perform classical computations
D) To enhance graphics processing
Answer: B) To model the way biological neurons communicate

Which technology is often used in neuromorphic chips to emulate synapses?
A) Flash memory
B) CMOS technology
C) Resistive switching
D) Magnetic storage
Answer: C) Resistive switching

What is the primary purpose of using analog components in neuromorphic systems?
A) To increase speed of computations
B) To replicate the continuous nature of biological signals
C) To reduce manufacturing costs
D) To enhance digital signal processing
Answer: B) To replicate the continuous nature of biological signals

Which of the following applications can benefit from neuromorphic computing?
A) Basic text editing
B) Autonomous robotics
C) Traditional database management
D) Simple arithmetic operations
Answer: B) Autonomous robotics

What is a characteristic of memristors used in neuromorphic computing?
A) They store information in a binary format
B) They can change resistance based on past voltage
C) They require constant power to retain information
D) They are exclusively digital devices
Answer: B) They can change resistance based on past voltage

Which of the following statements is true about neuromorphic hardware?
A) It strictly follows traditional CPU designs
B) It often operates asynchronously
C) It is primarily used for gaming applications
D) It relies solely on digital circuits
Answer: B) It often operates asynchronously

What role does learning play in neuromorphic computing?
A) It is not applicable to neuromorphic systems
B) It enhances performance through adaptation over time
C) It complicates the architecture
D) It only applies to software-based systems
Answer: B) It enhances performance through adaptation over time

What is one of the challenges of developing neuromorphic systems?
A) Lack of biological inspiration
B) Difficulty in simulating synaptic plasticity
C) Inability to process sensory information
D) Exclusively linear processing capabilities
Answer: B) Difficulty in simulating synaptic plasticity

Which of the following best describes “plasticity” in the context of neuromorphic computing?
A) The ability of a material to deform permanently
B) The capability of neural networks to adapt and learn from experience
C) The rigidity of circuit designs
D) The use of fixed weights in calculations
Answer: B) The capability of neural networks to adapt and learn from experience

What kind of data processing is neuromorphic computing particularly well-suited for?
A) Structured data processing
B) Unstructured data processing and sensory data
C) Batch processing
D) Simple data storage
Answer: B) Unstructured data processing and sensory data

Which of the following is a prominent neuromorphic computing platform developed by IBM?
A) Blue Gene
B) TrueNorth
C) Watson
D) Summit
Answer: B) TrueNorth

What is the significance of event-based processing in neuromorphic computing?
A) It allows for real-time responses to stimuli
B) It increases power consumption
C) It complicates data handling
D) It limits the types of applications
Answer: A) It allows for real-time responses to stimuli

Which of the following statements about energy efficiency in neuromorphic systems is true?
A) Neuromorphic systems are less efficient than traditional systems
B) They consume energy only during computation
C) They require continuous power for all operations
D) They are exclusively energy-inefficient
Answer: B) They consume energy only during computation

In neuromorphic computing, what does the term “neuroinspired algorithms” refer to?
A) Algorithms that mimic traditional computing methods
B) Algorithms designed to replicate cognitive functions of the brain
C) Randomized algorithms with no biological basis
D) Simple sorting algorithms
Answer: B) Algorithms designed to replicate cognitive functions of the brain

What is the primary function of a synapse in a biological neural network?
A) To transmit electrical signals
B) To store memory
C) To connect neurons and facilitate communication
D) To regulate energy levels
Answer: C) To connect neurons and facilitate communication

Which of the following components is essential for building neuromorphic systems?
A) Standard logic gates
B) Synaptic devices
C) Optical fibers
D) Classical memory units
Answer: B) Synaptic devices

What is a common method for implementing neuromorphic computing systems?
A) Using traditional software algorithms
B) Employing hardware designed to mimic neural processes
C) Relying on classical computer architecture
D) Implementing only digital logic
Answer: B) Employing hardware designed to mimic neural processes

Which type of learning is often implemented in neuromorphic systems?
A) Supervised learning only
B) Reinforcement learning and unsupervised learning
C) Classical programming techniques
D) Predictive modeling without adaptation
Answer: B) Reinforcement learning and unsupervised learning

What is one of the goals of neuromorphic computing research?
A) To enhance traditional CPU performance
B) To create systems that can perform cognitive tasks similar to humans
C) To replace all classical computing technologies
D) To eliminate the need for memory
Answer: B) To create systems that can perform cognitive tasks similar to humans

Which of the following describes a neuromorphic chip’s ability to process data?
A) It can only process binary data
B) It can handle continuous and discrete signals
C) It requires constant recalibration
D) It is limited to low-level computations
Answer: B) It can handle continuous and discrete signals

What is a potential application of neuromorphic computing in healthcare?
A) Basic data entry
B) Analyzing complex medical data for diagnostics
C) Traditional patient record management
D) Simple arithmetic operations
Answer: B) Analyzing complex medical data for diagnostics

Which of the following describes a benefit of using neuromorphic systems in robotics?
A) Increased power consumption
B) Enhanced real-time processing capabilities
C) Greater reliance on centralized processing
D) Exclusive focus on numerical data
Answer: B) Enhanced real-time processing capabilities

What is the significance of “in-memory computing” in neuromorphic systems?
A) It reduces data processing speed
B) It minimizes the need for data movement, improving efficiency
C) It requires constant power
D) It complicates system architecture
Answer: B) It minimizes the need for data movement, improving efficiency

Which of the following is a challenge when developing neuromorphic computing systems?
A) Lack of application diversity
B) Integration with existing digital systems
C) Excessive energy efficiency
D) Simplifying learning processes
Answer: B) Integration with existing digital systems

What is the goal of bio-inspired computing?
A) To eliminate all computational errors
B) To replicate natural cognitive processes in artificial systems
C) To enhance only numerical computations
D) To create traditional computing architectures
Answer: B) To replicate natural cognitive processes in artificial systems

What does “neuromorphic vision” refer to?
A) A method for analyzing data with traditional algorithms
B) The ability of neuromorphic systems to process visual data like biological systems
C) A type of virtual reality
D) A specific hardware design for vision processing
Answer: B) The ability of neuromorphic systems to process visual data like biological systems

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
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  • 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
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  • AI Accelerators MCQs