Neuromorphic Computing - Introduction

Neuromorphic Computers are quicker and more intelligent compared to traditional computing systems because it uses same architecture as human brain. In this section we will discuss detailed overview, necessity and features of neuromorphic computers.

What is Neuromorphic Computing?

Neuromorphic computing is the design and construction of computing systems that are inspired by the architecture and functioning of the human brain. This design consists of both processing and memory being governed by neurons and synapses as a single unit. On the other hand, traditional computers consist of separate sections for CPUs and memory.

Need of Neuromorphic Computers

To understand the need for neuromorphic computers, first let's go through the drawbacks of traditional von Neumann computers.

Traditional von Neumann computers rely on a sequential processing architecture where the CPU and memory are separate entities. This leads to a few significant limitations:

• Memory Bottleneck: The constant data transfer between the CPU and memory creates a bottleneck, slowing down processing speeds and increasing energy consumption.
• High Power Consumption: Von Neumann architectures require more power as they process data sequentially, leading to inefficiency, especially in large-scale data processing and real-time applications.
• Lack of Parallelism: Traditional systems process tasks sequentially, which limits their ability to handle multiple complex operations simultaneously, as the brain does.
• Limited Adaptability: Von Neumann computers cannot easily adapt or learn from new data without explicit reprogramming, making them inefficient for tasks that require continuous learning or pattern recognition.

Neuromorphic computers are designed to overcome these limitations. By mimicking the human brain's architecture, they offer higher efficiency, real-time processing, and the ability to perform complex tasks such as pattern recognition, decision making, and learning from unstructured data.

Key Features of Neuromorphic Computers

• Energy Efficiency: Neuromorphic systems consume significantly less power compared to traditional computing systems by utilizing event-driven processing, only activating neurons when necessary.
• Real-Time Processing: Neuromorphic architectures excel in processing large volumes of data in real time, which is crucial for applications like robotics and sensory systems.
• Parallel Processing: Like the brain, neuromorphic computers can handle many operations simultaneously, allowing them to process information much faster than traditional systems that operate sequentially.
• Adaptive Learning: These systems can learn from the environment and adapt over time, enabling them to improve performance on tasks without explicit reprogramming.
• Fault Tolerance: Neuromorphic computers are inherently more robust and fault-tolerant due to their decentralized architecture, reducing the impact of individual component failures.

Neuromorphic Computing Hardwares

Neuromorphic hardware are build using special components that can function like biological neurons and synapses. These systems can compete with the human brains computing efficiency, speed, and adaptability. Neuromorphic hardware can be broadly categorized into:

• Neuromorphic Chips: Custom-designed chips such as IBM's TrueNorth and Intel's Loihi are built to replicate the behavior of neurons and synapses, that works in low-power.
• Neuromorphic Sensors: Sensors that behave like biological sensory organs and interact more naturally with neuromorphic processors by generating sparse, asynchronous data. For example, event-driven cameras and tactile sensors.
• Memristor-based Systems: A memristor is a two-terminal electrical component that regulates the flow of electrical current in a circuit and remembers the amount of charge that has passed through it. Hardware based on memristors provide high-speed data storage and processing capabilities for neuromorphic computers.

Neuromorphic Computing Chips

Neuromorphic chips are the heart of hardware-based neuromorphic computing. Some of the most notable neuromorphic chips include:

• IBM TrueNorth: A neuromorphic chip designed to operate with very low power, TrueNorth contains over one million neurons and 256 million synapses. It is optimized for real-time sensory data processing and cognitive tasks.
• Intel Loihi: Loihi is another neuromorphic chip that mimics brain-like plasticity. It enables on-chip learning, meaning it can learn and adapt based on data without the need for retraining or cloud-based processing.
• SynSense DYNAP-SE: This is another bio-inspired neuromorphic processor designed for ultra-low-power sensory data processing, useful in applications like robotics, drones, and edge devices.

Applications of Neuromorphic Computers

Neuromorphic computers are being developed for a range of applications that require efficient real-time processing, adaptability, and power efficiency. Some key areas include:

• Robotics: Neuromorphic systems can process sensory data in real time, making them ideal for autonomous robots that need to interact with their environment quickly and efficiently.
• Healthcare: Neuromorphic systems are being explored for use in medical devices, such as neural implants for brain-computer interfaces and prosthetics that can respond to neural signals in real time.
• Smart Sensors: Neuromorphic systems can be integrated with sensors to create intelligent devices that process information locally, reducing the need for data to be sent to the cloud, which saves both time and energy.
• Artificial Intelligence: Neuromorphic computing can enhance AI capabilities, especially in applications requiring real-time learning, pattern recognition, and decision-making.
• Autonomous Vehicles: Neuromorphic chips can be employed in self-driving cars to process sensory input from cameras, LIDAR, and other systems in real time, improving the vehicle's decision-making and reaction speed.