Neuromorphic Computing | 14 Sep 2024

Source:TH 

Recently, the researchers at the Indian Institute of Science (IISc) have developed a neuromorphic or brain-inspired analog computing. 

• This system is capable of storing and processing data in 16,500 states using molecular film. 

What is Neuromorphic Computing? 

• About:  
  • 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. 
• Differences from Traditional 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.