#1 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.

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.

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.

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.

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.

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 realtime 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.