Spiking neural networks
The building block for AI innovation
Highlights
- Evolving neuromorphic processors, which are designed to replicate the human brain, is critical for enabling intelligence at the edge and processing sparse events.
- With neuromorphic computing, low latency real-time operations can be performed with significant reduction in energy costs.
- Advances in neuromorphic computing promise to ease the energy concerns associated with Dennard Scaling, Moore’s Law, and von Neumann bottleneck.
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What is an SNN
Spiking Neural Network: The building block for innovation
The Spiking Neural Network (SNN) is the third generation of neural network models, built with specialized network topologies that redefine the entire computational process. The spiking makes it more intelligent and energy-efficient, which is crucial for small devices to perform.
With a three-layered feedforward specialized network topology, the SNN is one of the most powerful neural networks that can process temporal data in real-time. This high computational power and advanced topology make it suitable for robotics and computer vision applications that require real-time data processing.
SNN facilitates real-time sourcing and processing of the data and is a major improvement over other neural networks, which primarily rely on frequency rather than temporal data.
SNN is one of the most powerful neural networks that can process temporal data in real-time.
The SNN spikes are computationally more advanced, and the firing activity of the neuron in the SNN architecture is not tied to static inputs but to the notion of time.
Core SNN architecture
How SNN's are all set to shape the future
The SNN is made of billions of very well-connected neurons through a three-layer mechanism. These three—input, hidden, and output layers—work in tandem to mimic the function of the human brain. In other words, there is no separation of data perception and processing.
Instead, the entire process happens at once because the input layer’s vectors are connected to that of the hidden, which is in turn connected to the output layer. This interconnectivity ensures the continuous transmission of signals between the neurons. Among these layers, the hidden layers (there can be multiple) are the most significant ones, as this is where the convolution process takes place.
Together, the convolutional capabilities and highly connected layers take image and video processing to the next level, particularly in medical image analysis and natural language processing. These critical applications require advanced classification and processing, often in real-time.
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Use cases for SNN
SNN's specialized network topology unleashes numerous possibilities in the world of robotics and computer vision
The SNN has created a lot of excitement in the AI community because of its specialized network topology that unleashes numerous possibilities in the world of robotics and computer vision. The biggest advantage is SNN’s in-memory computing using neuromorphic hardware.
The human brain’s efficiency comes from its ability to store and process information from the same organ. In the case of machines, the Von Neumann bottleneck creates inefficiency and latency because the memory and processing units are separate.
So, every time data is received, it is stored in the memory and then accessed by the processing unit, which creates a delay. The SNN eliminates this two-step process through in-memory computing. Since there is no passage of information from the memory to the processing unit, the latency is reduced. Also, the energy consumption is much lower when neuromorphic hardware is used.
About the hardware
SNN to lower energy consumption
The SNN’s function closely emulates the human brain and therefore encodes temporal data, by which it introduces the concept of time along with other elements. This ensures lower energy consumption, particularly when neuromorphic hardware is used.
The neuromorphic hardware enables better performance because it simulates neurons, which is essential to stimulate differential equations to leverage the discreet and sparse behavior of the neurons. As regular hardware is not designed to handle this behavior, it can be less inefficient. Spiking Neural Networks and their classification capabilities have been tested. According to a study, the trained neurons can run the classification even when the stimuli and corresponding decision times are segregated while there is simultaneous neural activity. This has been tested on the Statlog Landsat and Iris datasets in various experiments.
Conclusion
SNN spells a breakthrough for the robotics sector
The SNN undoubtedly spells a breakthrough for the robotics sector because, unlike AI chips, the SNNs emulate the original language of human intelligence to interpret real-world data. This is precisely what robotics needs to perform tasks that are normally executed by human beings. Until now, they could only perform redundant tasks, but with SNN, that is bound to change.
Nevertheless, it cannot be denied that SNN only lays the foundation and is not perfectly aligned with human brain functions. That’s because the neurons in the various layers of the human brain adjust and auto-readjust themselves based on the situation. Although the SNN’s layering gives neurons a certain degree of individuality, it is far from becoming human-like.
