Neuromorphic Computing: How the human brain inspires AI?
The advancement in artificial intelligence (AI) have been awe-inspiring. In a wide range of domains, artificial intelligence has already transformed the way we work and live. From biometric systems (e.g. face recognition and voice recognition) to virtual assistants like Alexa, Siri, Google Assistant, etc… to large scale data security and fraud detections, as of now in all aspects AI has become an integral part of our daily lives.
Yet, there will be a large gap between the current AI and truly human-like intelligence. There are many tasks that a human may naturally do, even in a subconscious level, which current-generation artificial intelligence will struggle with. For an example, our brains are more flexible enough to see, hear, smell, feel, walk, talk, empathize, reason, and form memories all at once. Perhaps there is a bridge gap between what AI can currently do and the next level of AI.
” Neuromorphic computing observes, how the neural systems of the human brain interact and uses this information to take part in computing and Artificial Intelligence (AI) into the next level.”
The Neuromorphic computing uses a new algorithmic approaches that emulates, how the human brain interacts with the world to deliver the capabilities closer to human cognition. Neuromorphic computing’s is an innovative architectural approach will power the future autonomous AI solutions that require energy efficiency and continuous learning. It’s a promise to open the exciting new possibilities in computing and is already used in a variety of areas including, sensing, robotics, healthcare, and large-scale AI applications.
Neuromorphic Computing: Computing that imitates human brains
The human brain performs computation are vastly different manner than the current computers. For an instance, today computer systems have separate chip components to store the memory and process information. The speed of data while moving between the memory and the processor is often a limiting factor in computing called the “Von Neumann bottleneck”. However, computation in the brain and performance in locally are asynchronously and in parallel. Memory, learning and processing in the brain are all located in the neurons and synapses. Neuromorphic computing has a potential to bring us faster, much more energy efficient computing and perhaps even the next generation of AI.
The AI generation follows mainly the concern with perception and sensing. A Deep Learning Network (DLN) that analyzes the content from video or image data would be a good example here. The Deep Neural Networks have already arrived in the application by means of classical technologies such as SRAM or Flash-based and imitate the parallelism and efficiency of the brain.
From artificial neural networks to spiking neural networks
Current generation of AI and machine learning make heavy use of artificial neural networks to model data. These networks drawn the inspiration from our brains and work by sending the numerical values from neuron to neuron. Unlike the artificial neural networks, our brains communicate the signals from neuron to neuron via a train of electrical spikes. Spiking neural networks are naturally implemented only on neuromorphic hardware, the model spiking nature of the brain and are a better approximation of realistic brain activity than current generation artificial neural networks.
Spiking neural networks (SNNs), a novel models that simulate the natural learning by dynamically re-mapping the neural networks and are used in neuromorphic computing to make the decisions in response to learned patterns over time.
Inspiration from the human brain
Human brain has a baseline energy footprint about 20 watts; it gives the brain power to process the complex tasks in milliseconds. Today’s technology CPUs and GPUs dramatically outperform the human brain for serial processing tasks. However, the process of moving data from the memory to a processor and back creates latency and, in addition, expends enormous amounts of energy.
One of the major challenges in neuromorphic research is to get at human flexibility. The neuromorphic computing platform is designed for computational neuroscience and machine learning. Neuromorphic systems attempt to imitate how the human nervous system operates in human body. In this field engineering tries to imitate the structure of biological sensing and information processing nervous systems. In other words, neuromorphic computing implements the aspects of biological neural networks as analogue or digital copies on electronic circuits.
Neuromorphic computing gets its inspiration from the human brain with the goal of designing computer chips that are able to merge memory and processing. In the human brain, synapses provides a direct memory access to the neurons that process all information.
While neuromorphic technology is still in its fantacy, the field is advancing rapidly. In future, commercially available the neuromorphic chips will most likely have an impact on edge devices, robotics, and Internet of Things (IoT) systems.
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