In traditional computations, the information is processed in different ways than a human can do. Some conventional systems work on separate memory and the processing units. These conventional systems work in clock-driven and sequential manner. The brain can perform event-driven computation using the networks of neurons and synapses. Some of the best computer engineering colleges in Maharashtra have included neuromorphic computing in the course curriculum to help students understand this topic in context of modern computing. Let us further explore neuromorphic computing:
What is neuromorphic computing?
This is an approach which attempts to design the computing systems modeled as per the structure and the functioning of the biological neural systems. Hardware architecture of the neurals reflects the working of the human brain.
This blog provides a technical yet human-readable explanation of neuromorphic computing — without diving into machine-level code or assembly concepts — focusing on architecture, principles, advantages, challenges, and real-world implementations.
Why to Rethink about Traditional Computing?
Sometimes the conventional computers follow Von-Neumann Architecture.
- Memory and processing are physically separated.
- Data continuously moves between CPU and memory.
- Computation is clock-driven and sequential.
This design leads to:
- High power consumption
- Latency due to memory bottlenecks
- Inefficiency in parallel pattern recognition tasks In contrast, the human brain:
- Consumes only ~20 watts
- Contains ~86 billion neurons
- Operates using event-based signaling
- Processes information in parallel Neuromorphic computing attempts to bridge this gap.
Core Principles of Neuromorphic Computing
1. Spiking Neural Communication
Unlike traditional artificial neural networks that use continuous values, neuromorphic systems use spikes — discrete electrical events — similar to how biological neurons fire.
A neuron:
- Integrates incoming signals
- Fires only when a threshold is crossed
- Sends a spike to connected neurons
This event-driven mechanism makes computation energy-efficient because activity occurs only when necessary.
2. Co-location of Memory and Computation
In biological brains:
- Synapses store weights (memory)
- Neurons process signals
- Both exist in the same physical network
The hardware under neuromorphic computing can directly process the elements, reduce the data transfer overhead and eliminate the traditional memory bottleneck.
3. Massive Parallelism
Brain-like systems operate with millions of neurons simultaneously. These neuromorphic chips can be used for computing the neurons at a time.
Architecture of a Neuromorphic System
A neuromorphic chip generally consists of:
- Artificial neurons – Processing units that accumulate inputs
- Artificial synapses – Weighted connections between neurons
- Spike routing fabric – Communication network between neurons
- Learning circuits – Implement adaptation rules
Unlike CPUs, there is no central control. Computation is distributed and asynchronous.
Biological Inspiration
Neuromorphic engineering is inspired by neuroscience research. It draws from:
- Neuron membrane dynamics
- Synaptic plasticity
- Spike-timing dependent plasticity (STDP)
- Cortical microcircuits
The goal is not to replicate the brain perfectly but to abstract its computational efficiency.
Hardware Implementations
There are some research and industrial organisations that have developed neuromorphic processors:
- Loihi, a chip for spiking neural networks has been developed by Intel
- TrueNorth, which has one million programmable neurons, is developed by IBM
- A massively parallel brain simulation platform has been developed by SpiNNaker
These systems are optimised for:
- Ultra-low power consumption
- Real-time sensory processing
- Adaptive learning
Learning in Neuromorphic Systems
Neuromorphic systems often use the following rules instead of relying on backpropagation and large datasets:
1. Local Learning Rules
Each synapse gets updated based on local activity.
2. Spike-Timing Dependent Plasticity (STDP)
In an attempt to make the connection between the neurons stronger, the presynaptic neuron has to fire before the postsynaptic neuron, and in an attempt to make the connection between the neurons weaker, the postsynaptic neuron has to be fired first.
Applications
Neuromorphic computing is especially promising in areas requiring:
1. Edge AI
There are some low-power smart sensors used in IoT devices.
2. Robotics
In neuromorphic computing there is Real-time perception and motor control available.
3. Brain–Machine Interfaces
Adaptive signal interpretation.
4. Autonomous Systems
Energy-efficient event-driven vision processing.
Advantages
- Extremely low energy consumption
- High parallel efficiency
- Real-time processing
- Event-driven architecture
- Scalable neural emulation
Technical Challenges in Neuromorphic Computing
Neuromorphic computing is facing the following challenges now days:
- Limited standardised programming models
- Lack of mature software ecosystems
- Hardware complexity
- Scalability constraints
- To train the large networks is difficult
Comparison with Deep Learning Accelerators
| Feature | GPUs/TPUs | Neuromorphic Chips |
| Processing Type | Synchronous | Asynchronous |
| Energy Usage | High | Very Low |
| Data Flow | Continuous | Event-driven |
| Architecture | Centralised | Distributed |
Neuromorphic systems have not replaced the GPUs whereas they have worked as complementary technologies for the different tasks.
Future Directions
Research trends include:
- Integration of memristor-based synapses
- Hybrid AI systems combining deep learning with spiking models
- On-chip learning capabilities
- Brain-scale simulation
Following countries have shown their interest to invest heavily in neuromorphic research:
- United States
- Germany
- Japan
Conclusion
The neuromorphic computing represents the working in the flow of instruction-driven to event-driven intelligence. A pathway of energy-efficient and adaptive computing systems is represented here with the help of parallelism and integrated memory usage. The hardware used in neuromorphic computing can define the term of intelligent systems which can consume the least power and will be working in real time.
Pursuing a B.Tech in Computer Engineering from a globally-recognised college can boost your understanding of neuromorphic computing at a professional level. The journey of the artificial neurons is not only of biology but also to understand the principles behind computing and this journey has been travelled over millions of years.