Intel self-learning chip mimics human brain

Intel self-learning chip mimics human brain

Technology News |
By Rich Pell

The chip is capable of representing  130,000 neurons and 130 million synapses Intel said.

Unlike convolutional neural network (CNN) and other deep learning processors the Loihi test chip uses an asynchronous spiking model to mimic neuron and synapse behavior in a much closer analog to animal brain behaviour. This is similar to the work of startup BrainChip Inc although for now BrainChip is offering a solution based on an FPGA implementation (see BrainChip launches neuromorphic hardware accelerator).

Machine learning models based on CNNs use large training sets to set up recognition of objects and events. However, unless their training sets have specifically accounted for a particular element, situation or circumstance, these machine learning systems do not generalize well.

Intel’s neuromorphic chip – the Loihi test chip –  mimics how the brain functions by learning to operate based on various modes of feedback from the environment. Such a system does not need to be trained in the traditional way and can improve its performance over time.

Intel said it would be sharing the Loihi test chip with leading university and research institutions with a focus on advancing AI in the first half of 2018.

The chip has an asynchronous neuromorphic many core mesh that supports a 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.


Related articles:
ST preps second neural network IC
Imagination launches flexible neural network IP
BrainChip launches neuromorphic hardware accelerator
China chip startup nets $100 million Series A

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