Human Brain-Inspired Computing Platform by IISc: A Revolutionary Boost to AI

Human Brain-Inspired Computing Platform by IISc: A Revolutionary Boost to AI

Sub: Sci

Sec: Awareness in IT

Why in News

The Indian Institute of Science (IISc) has made a groundbreaking technological advancement in Artificial Intelligence (AI). Researchers at IISc have developed a new brain-inspired computing platform, which offers the potential to significantly enhance AI tools and computing power.

Key Developments and Breakthroughs

Introduction to the New AI Platform: Researchers from IISc’s Centre for Nano Science and Engineering (CeNSE) have created an analogous computing platform capable of mimicking the human brain. This platform, embedded in a molecular film, showcases functions like data processing and storage, similar to brain-like operations.

High Conductance States in Molecular Film: The molecular film developed by IISc researchers provides 16,500 conductance states. In contrast, traditional digital computing operates on binary states (0 and 1), requiring more energy and time, limiting the speed and efficiency of current AI tools.

Advancement in Neuromorphic Computing: The new platform represents a major step in neuromorphic computing, which follows human brain-like computing techniques. It addresses many limitations in present-day digital computing, making AI tasks more efficient and flexible for deployment on personal electronic devices like smartphones, laptops, and desktops.

About Analogous Computing Platform

An analogous computing platform mimics the way the human brain processes and stores information. Unlike traditional digital computing, which relies on binary states (0 and 1), analogous computing systems operate in a continuous spectrum of values, much like the brain’s neural networks.

This platform is embedded in a molecular film that behaves similarly to biological neural networks. It stores and processes information by tracking the movement of ions or molecules within the film. These molecular movements simulate the brain’s complex signaling pathways, allowing the system to replicate brain-like functions such as multi-state memory, adaptive learning, and real-time processing.

Key features include:

Multiple Conductance States: Traditional computers have two states, but this molecular film offers 16,500 conductance states, providing a vast range of intermediate values and improving the complexity and flexibility of data processing.

Neuromorphic Traits: By controlling molecular transitions with kinetic control, the platform achieves neuromorphic traits, allowing it to mimic the brain’s synaptic functions more accurately.

How It Works:

Free Ionic Movement: The movement of ions within the molecular film is analogous to the flow of signals in the human brain’s neurons.

Memory Pathways: The molecular film expands memory storage capacity by creating unique states, as opposed to binary memory systems.

Efficient Processing: With nanosecond voltage pulses, it can control molecular kinetics, enabling efficient memory and processing operations without the high energy demands of digital systems.

About Molecular Film:

A molecular film is a thin layer of material composed of molecules that are specifically arranged to exhibit particular properties, such as electrical, optical, or magnetic behavior. These films are typically just a few nanometers to micrometers thick and can be engineered to perform specific tasks by manipulating their molecular structure.

Key Features of Molecular Films:

Thin Structure: Molecular films are incredibly thin, often only a few molecules thick, which allows for precise control of their properties.

Functionalized Molecules: The molecules in the film are carefully chosen or engineered to exhibit desired characteristics such as conductivity, light sensitivity, or responsiveness to voltage.

Electrical Conductance: In the context of neuromorphic computing, molecular films are used for their ability to conduct electrical signals and create multiple conductance states. This mimics the function of biological synapses in the brain.

About Neuromorphic Computing:

Neuromorphic computing mimics the brain’s structure to enhance AI efficiency. It uses spiking neurons and synapses to replicate how biological neurons communicate and learn.

Spiking Neural Networks (SNNs): Neurons store and process data, spiking when their charge reaches a threshold. This spike sends signals across synapses, mimicking brain activity.

Synapses: Synapses in neuromorphic systems are represented by transistor devices that adjust their weights, enabling learning and adaptability.

Event-Driven: Unlike traditional systems, neuromorphic computing processes information only when neurons spike, saving energy.

Neuromorphic computing allows for real-time, brain-like learning with improved energy efficiency.