- 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 - Difference From Traditional Computing
Traditional computers, which we use, follow a sequential processing architecture called the Von Neumann architecture. This design consists of separate CPUs and memory units, where data is stored in the memory and transferred to the CPU whenever necessary.
Neuromorphic computers, on the other hand, follow a parallel processing architecture that is inspired by the human brain and is composed of neurons and synapses. This kind of architecture is highly energy-efficient and has broader capabilities. Let's dive deeper into the architectural differences between traditional computers and neuromorphic computers in the section below.
Von Neumann vs Neuromorphic Architecture
As mentioned above, traditional computers with Von Neumann architecture feature a straightforward design, whereas neuromorphic computers have a complex architecture that mimics the human brain. The following image shows the flowchart of Von Neumann and neuromorphic computer architectures.
In below section we tabulated differences between Von Neumann architecture and Neuromorphic architecture.
| Specification | Von Neumann Architecture | Neuromorphic Architecture |
|---|---|---|
| Operation | Uses sequential processing operations, where instructions are executed one at a time, which can cause bottlenecks. | Uses parallel processing, mimicking the brains neural networks, allowing for multiple tasks to be handled simultaneously. |
| Processing Units | Has separate units for CPU (processing) and memory, requiring data to be constantly transferred between them. | Processing and memory are integrated together in the form of neurons and synapses, allowing faster access to data. |
| Power Consumption | Consumes more power due to constant data transfer and sequential operations. | Highly energy efficient, consuming power only when neurons are triggered (event-driven processing). |
| Data Handling | Processes data in a linear fashion, which can limit performance when handling complex tasks like pattern recognition. | Can handle complex tasks such as pattern recognition, decision making, and learning in real-time by processing large volumes of data simultaneously. |
| Fault Tolerance | More prone to faults since failure of a component like a CPU or memory can halt the system. | Highly fault-tolerant, as the decentralized nature of neurons allows the system to function even if some components fail. |
| Learning Ability | Requires explicit programming and cannot learn from data without human intervention. | Can adapt and learn from data over time, improving its performance on tasks autonomously. |
| Applications | Primarily used in general-purpose computing tasks, including everyday applications like word processing and basic calculations. | Ideal for advanced applications such as AI, robotics, sensory systems, and tasks requiring cognitive functions. |