- Neuromorphic Computing - Home
- Neuromorphic Computing - Introduction
- Neuromorphic Computing - Difference From Traditional Computing
- Neuromorphic Computing - History and Evolution
- Neuromorphic Computing - Types of Technologies
- Neuromorphic Computing - Architecture
- Neuromorphic Computing - Memristors
- Neuromorphic Computing - Synaptic Devices
- Neuromorphic Computing - Hardware Accelerators
- Neuromorphic Computing - Neuromorphic Chips
- Neuromorphic Computing - Analog Circuits
- Neuromorphic Algorithms and Programming
- Neuromorphic Computing - Spiking Neural Networks (SNNs)
- Neuromorphic Computing - Algorithms for SNNs
- Neuromorphic Computing - Programming Paradigms
- Applications of Neuromorphic Computing
- Neuromorphic Computing - Edge Computing
- Neuromorphic Computing - IoT
- Neuromorphic Computing - Robotics
- Neuromorphic Computing - Autonomous Systems
- Neuromorphic Computing - AI and ML
- Neuromorphic Computing - Cognitive Computing
- Neuromorphic Computing Resources
- Neuromorphic Computing - Useful Resources
- Neuromorphic Computing - Discussion
Neuromorphic Computing - Types of Technologies
Neuromorphic computers have attempted to build using various technologies at both hardware and software levels. In this section, we will explore the different types of technologies that that can be used to build neuromorphic systems.
Types of Neuromorphic Computers
Neuromorphic computers can be classified into two types: hardware-based neuromorphic computers and software-based neuromorphic computers. Each type aims to emulate the human brain's architecture, but through different approaches. Let's explore each type in detail.
Hardware Based Neuromorphic Computers
Hardware-based neuromorphic computers are built using special physical components that can mimic the behavior of biological neural systems. These systems are more energy-efficient and good a t decision making than software systems. As of now we have following hardware-based neuromorphic systems.
- Analog Neuromorphic Chips: These chips use analog circuits to simulate the continuous signals and dynamics of biological neurons. Which means they are not dependent on 0s and 1s for calculations as there in traditional computers. They excel in tasks that require real-time, energy-efficient processing, such as sensory systems in robotics or low-power devices for real-time data analysis. Learn more about analog circuits.
- Memristors: 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. They have a type of non-volatile memory that can store and process information simultaneously, much like neurons. Learn more about memristors.
Software Based Neuromorphic Computers
Software-based neuromorphic computers simulate brain-like processes using algorithms an that can be implemented on traditional hardware such as CPUs or GPUs. The brain-like algorithms and architectures can achieve higher efficiency and better performance in specific tasks.
- Spiking Neural Networks (SNNs): SNNs are a type of artificial neural network algorithm that functions as similar to biological neurons by processing data through discrete spikes. Learn more about SNNs.
- Neuromorphic Systems: Neuromorphic systems refer to software platforms designed to replicate neural processes, often used for machine learning, pattern recognition, and adaptive learning tasks. These systems are typically used in research to explore brain-like architectures on traditional computing hardware before deploying them to specialized neuromorphic hardware.