Latest Latest Updates

EDITORIALS & ARTICLES

Neuromorphic Computing

    • Neuromorphic computing is a process designed to mimic the structure and function of the human brain using artificial neurons and synapses. 
    • It represents a significant shift from traditional binary computing to neuromorphic computing, allowing systems to learn from the environment. 
  • Working Mechanism: 
    • It involves the use of Artificial Neural Networks (ANN) made up of millions of artificial neurons, similar to those in the human brain. 
    • These neurons pass signals to each other in layers, converting input into output through electric spikes or signals, based on the architecture of Spiking Neural Networks (SNN). 
      • This allows the machine to mimic the neuro-biological networks in the human brain and perform tasks efficiently and effectively, such as visual recognition and data interpretation. 
  • Key Features: 
    • Brain-Inspired Design: Neuromorphic systems replicate the brain''s architecture, particularly the neocortex, which is responsible for higher cognitive functions such as sensory perception and motor commands. 
    • Spiking Neural Networks: These systems use spiking neurons that communicate through electrical signals, closely resembling biological neuronal behavior. This design allows for parallel processing and real-time learning. 
    • Integration of Memory and Processing: Unlike traditional von Neumann architecture, which separates memory from processing, neuromorphic systems integrate these functions, enhancing computational efficiency. 
  • Advantages: 
    • It allows computers to process information more efficiently, enabling faster problem-solving, pattern recognition, and decision-making compared to traditional computing systems. 
    • It has the ability to revolutionise AI hardware, enabling complex tasks like training Large Language Models (LLMs) on personal devices, addressing hardware limitations and energy inefficiencies. 
      • Current AI tools are restricted to resource-heavy data centers due to a lack of energy-efficient hardware. 
  • Integration with Molecular Film: 
    • Molecular films are ultrathin layers of molecules that can be engineered to exhibit specific electrical and optical properties, enabling the creation of brain-inspired data storage and processing devices. 
    • This film acts as a neuromorphic accelerator, simulating brain-like parallel processing to quickly handle complex tasks like matrix multiplication and improve computer performance when combined with silicon chips. 
    • The recent advancement involves a molecular film offering 16,500 possible states, surpassing traditional binary systems.  
      • This film utilizes molecular and ionic movements to represent memory states, mapped via precise electrical pulses, creating a "molecular diary" of states. 
A neuromorphic chip imitates the brain through elements that implement “neurons”, nodes that process information, and “axons”, connections with weights between the nodes that transfer electrical signals using analog circuitry.
  • Differences from Traditional Computing: 

Neuromorphic Computing

    • Parallel Processing: Neuromorphic computers can process multiple streams of information simultaneously, unlike traditional computers that operate sequentially. 
    • Energy Efficiency: They consume less power by computing only when relevant events occur, making them ideal for applications requiring real-time data processing. 
    • Traditional binary computing operates with bits in two states: 0 or 1, similar to a light switch being on or off. In contrast, analog computing uses continuous values, similar to a dimmer switch with a range of brightness levels. 






POSTED ON 14-09-2024 BY ADMIN
Next previous

General Studies

Political Science and International Relations

Sociology

Some Reference Essays

List of previous year essay topics (1997-2019)