Neuromorphic computing, also known as neuromorphic engineering, is an approach that mimics the human brain’s way of processing information. It involves designing hardware and software that simulate the brain’s neural and synaptic structures and functions, creating systems that can process data in a biologically inspired manner.
Neuromorphic computing
While neuromorphic computing might seem like a new field, its origins trace back to the 1980s. Pioneers Misha Mahowald and Carver Mead developed the first silicon retina, cochlea, and early silicon neurons and synapses that laid the foundation for the neuromorphic computing (NC) paradigm. This groundbreaking work set the stage for a more advanced understanding of how to design systems that closely resemble the brain’s operation.
As artificial intelligence (AI) systems continue to scale, they will require advanced hardware and software to keep up. NC could serve as a crucial accelerator for AI’s growth, improving performance in high-speed computing. Ongoing experiments are even exploring the potential of combining neuromorphic computing with quantum computing to push technological boundaries further.
According to Gartner, neuromorphic computing is one of the most promising new technologies for businesses. Professional services firm PwC also highlights the importance of exploring this technology. However noting that while it’s rapidly advancing, it hasn’t yet matured enough for widespread adoption.
Drawing inspiration from the human brain, neuromorphic computing heavily relies on biology and neuroscience. Neurons, the fundamental units of the brain and nervous system, play a key role. When activated, they trigger the release of chemical and electrical signals through a network of synapses, enabling communication between neurons. These biological mechanisms are replicated in neuromorphic systems through spiking neural networks (SNNs), which are a type of artificial neural network composed of spiking neurons and synapses. These systems closely model the brain’s natural processes, bringing us closer to creating more intelligent, efficient computational systems.
