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Basically, Neuromorphic Engineering was founded by Carver Mead, a professor from California Institute Of Technology(CalTech) who was known for his foundational work in semiconductor design. The combination of chip expertise, physics and biology yielded an environment for new ideas. Then the idea was very simple but revolutionary.

Intel Introduced new test learning chip “Loihi”, that is capable to mimic the functions of brain by learning to operate from various mode of feedback from the environment. The best part is — chip doesn’t need to be trained in traditional way. Also, its very power efficient, uses the data to learn and make inferences. It also get smarter as time passes using data from various sources.

Dr. Michael Mayberry wrote in editorial —

The potential benefits from self-learning chips are limitless. One example provides a person’s heartbeat reading under various conditions – after jogging, following a meal or before going to bed – to a neuromorphic-based system that parses the data to determine a “normal” heartbeat. The system can then continuously monitor incoming heart data in order to flag patterns that do not match the “normal” pattern. The system could be personalized for any user.

This is a big breakthrough in AI chip designing which is just a first step in chip designing with 130,000 neurons and 130 million synapses using 14nm process technology.

Some of the major highlights are as follows (as given in editorial):

Fully asynchronous neuromorphic many core mesh that supports a wide range of sparse, hierarchical and recurrent neural network topologies with each neuron capable of communicating with thousands of other neurons.

Each neuromorphic core includes a learning engine that can be programmed to adapt network parameters during operation, supporting supervised, unsupervised, reinforcement and other learning paradigms.

Fabrication on Intel’s 14 nm process technology.

A total of 130,000 neurons and 130 million synapses.

Development and testing of several algorithms with high algorithmic efficiency for problems including path planning, constraint satisfaction, sparse coding, dictionary learning, and dynamic pattern learning and adaptation.

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