زبان تخصصی کامپیوتر – درس هفتم
رایانش نورومورفیک
Neuromorphic chips are becoming one of the most influential technologies in the field of advanced computing, and their development is being accelerated by the growing demand for efficient and brain-inspired hardware. These chips are being designed to imitate the structure and behavior of biological neural networks, and their internal architecture has been shaped by decades of research in neuroscience and machine learning. In many laboratories, scientists are exploring how neurons and synapses are communicating in the brain, and this knowledge is being applied directly to the design of neuromorphic circuits. For years, traditional computing architectures have been facing serious limitations in terms of energy consumption, data-processing speed, and scalability. Because of these challenges, new hardware solutions have been developed, and neuromorphic chips have been considered one of the most promising answers. Many AI systems are currently being trained on GPUs and TPUs, but researchers have been reporting that certain learning tasks are being performed more naturally and more efficiently on neuromorphic hardware. This improvement has been achieved because the chips are operating in parallel and are processing signals in an event-driven manner, similar to how the human brain is reacting to stimuli.
In real-time applications such as autonomous driving, robotics, or smart surveillance, large amounts of sensory data are being generated every second. Neuromorphic chips are being used to process this data continuously while maintaining very low energy usage. Several prototypes have already been tested in mobile robots, and the robots were being trained to navigate complex environments using minimal power. These experiments have shown that real-time decision-making can be improved significantly when neuromorphic circuits are integrated into the system. One of the strongest advantages of neuromorphic processors is that they have been designed to support on-device learning. For example, in many research projects, small neuromorphic modules are being embedded inside IoT sensors, and these sensors have been used to analyze data locally instead of sending it to the cloud. This distributed intelligence has been considered a major step toward privacy-preserving AI, because sensitive data is not being transferred across networks. As a result, many industrial and medical devices have already been equipped with neuromorphic components to ensure continuous and secure monitoring. In healthcare, wearable sensors with neuromorphic circuits are being developed to monitor patient conditions in real time. These sensors are processing signals such as heartbeat variations and movement patterns, and the analysis is being performed instantly. Some clinical research tools have been used to predict early signs of neurological disorders, and their performance has been improved because neuromorphic chips are able to detect subtle patterns that were previously being overlooked by conventional processors. The global technology industry has been investing heavily in neuromorphic computing over the last decade. Several companies have introduced experimental platforms, and new generations of neuromorphic chips have been announced regularly. These platforms have been used in research on vision systems, speech recognition, natural-language processing, and adaptive robotics. Many scientific papers have reported that latency has been reduced, energy efficiency has been increased, and overall AI performance has been improved when neuromorphic hardware is applied correctly. In international conferences, new device architectures are being demonstrated every year, and the competition to create faster and more biologically accurate systems is becoming stronger. At the same time, material-science researchers are exploring new ways to build artificial synapses using advanced nano-materials. Some of these experimental synapses were being tested in flexible electronics, and several prototypes have already been presented as fully working neuromorphic systems. These innovations are showing how the future of AI hardware is being shaped by the intersection of biology, physics, and engineering. Overall, neuromorphic technology has been considered a key enabler for the next generation of intelligent machines. Its potential has been recognized across many industries, and the progress made in recent years has been remarkable. Although many challenges still exist, the field has been moving quickly, and the entire ecosystem of hardware, software, and research tools is being expanded continuously. With each new development, it is becoming clear that neuromorphic chips are not only transforming how machines are learning but also redefining the future of computing itself.
Conceptual Questions
1- Why are neuromorphic chips considered essential for the future of computing, and what underlying limitations of traditional hardware make their development necessary?
2- Based on the text, which specific characteristics of the human brain have influenced the design of neuromorphic chips, and how do these characteristics differentiate their performance from conventional processors?
3- Why do neuromorphic systems provide superior capabilities in real-time data processing compared to traditional architectures?
4- According to the text, what relationship exists between neuromorphic chips and the reduction of power consumption in robots and IoT devices?
5- What underlying mechanism in neuromorphic hardware allows for improved pattern recognition and decision-making abilities?
6- Why can on-device processing supported by neuromorphic chips lead to stronger privacy and security, as implied in the text?
7- How does the learning behavior of neuromorphic systems resemble biological learning in the brain, and why does this make them suitable for continuous learning tasks?
8- Why are industries such as healthcare, robotics, and security particularly interested in adopting neuromorphic technology, according to the text?
9- How might advancements in new materials and nano-materials influence the future development of neuromorphic chips?
10- The text states that neuromorphic technology is reshaping the future of computing. Based on the ideas presented, what are three key reasons supporting this claim?
1. According to the text, why are neuromorphic chips valuable for real-time applications?
A. Because they use larger memory units than GPUs
B. Because they process data event-by-event in a brain-like manner
C. Because they can only work in cloud-based systems
D. Because they are designed to replace all traditional processors
2. What is one major reason industries are investing in neuromorphic hardware?
A. It completely removes the need for machine-learning algorithms
B. It reduces energy consumption while improving responsiveness
C. It requires no training process for AI models
D. It works only with special operating systems
3. Why is on-device learning considered important in the text?
A. It makes the devices heavier
B. It prevents devices from storing any data
C. It allows data processing without sending sensitive information to external servers
D. It increases the cost of every device significantly
4. What role do new nano-materials play in neuromorphic-chip development?
A. They limit the flexibility of neuromorphic circuits
B. They enable new types of artificial synapses for future devices
C. They remove the need for electrical components
D. They make chips slower but easier to manufacture
5. Which statement reflects the text’s view on the future of neuromorphic technology?
A. It will mainly be used only in academic laboratories
B. It is likely to become a major component of next-generation intelligent machines
C. It will replace neuroscience research entirely
D. It will remain experimental without practical applications
6. Which sentence is in the present continuous tense?
A. Neuromorphic systems have been used in robotics.
B. The new chip was designed last year.
C. Scientists are exploring new materials for artificial synapses.
D. Many devices have already been tested.
7. Which sentence is in the present perfect passive tense?
A. Data is being processed in real time.
B. New prototypes have been developed in recent years.
C. New nano-structures have been explored by researchers.
D. Sensors were being tested during the experiment.
8. Which option shows the past continuous tense?
A. The system was being improved by engineers.
B. The robots were learning to navigate environments.
C. The chip has been designed for low-power tasks.
D. AI models are being trained on new platforms.
9. Which sentence is written in the present passive form?
A. Researchers are testing new hardware.
B. Privacy is improved when data stays on the device.
C. Scientists were developing new learning algorithms.
D. The robot was being controlled remotely.
10. Identify the sentence in the present perfect tense.
A. The device is being used in medical monitoring.
B. Engineers were testing the prototype.
C. Many companies have invested in neuromorphic computing.
D. Data is processed in parallel.