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Monday, September 25, 2017
Intel's New Loihi Self-Learning Chip Promises to Accelerate Artificial Intelligence


As part of an effort within Intel Labs, Intel has developed a self-learning neuromorphic chip - the Loihi test chip - that mimics how the brain functions by learning to operate based on various modes of feedback from the environment.

This energy-efficient chip, which uses the data to learn and make inferences, gets smarter over time and does not need to be trained in the traditional way. It takes a novel approach to computing via asynchronous spiking.

Neuromorphic computing draws inspiration from our current understanding of the brain's architecture and its associated computations. The brain's neural networks relay information with pulses or spikes, modulate the synaptic strengths or weight of the interconnections based on timing of these spikes, and store these changes locally at the interconnections. Intelligent behaviors emerge from the cooperative and competitive interactions between multiple regions within the brain's neural networks and its environment.

Machine learning models such as deep learning have made tremendous recent advancements by using extensive training datasets to recognize 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.

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 type of logic could also be applied to other use cases, like cybersecurity where an abnormality or difference in data streams could identify a breach or a hack since the system has learned the "normal" under various contexts.

Intel says that the Loihi research test chip includes digital circuits that mimic the brain's basic mechanics, making machine learning faster and more efficient while requiring lower compute power. Neuromorphic chip models draw inspiration from how neurons communicate and learn, using spikes and plastic synapses that can be modulated based on timing. This could help computers self-organize and make decisions based on patterns and associations.

The Intel Loihi test chip offers highly flexible on-chip learning and combines training and inference on a single chip. This allows machines to be autonomous and to adapt in real time instead of waiting for the next update from the cloud. Researchers have demonstrated learning at a rate that is a 1 million times improvement compared with other typical spiking neural nets as measured by total operations to achieve a given accuracy when solving MNIST digit recognition problems. Compared to technologies such as convolutional neural networks and deep learning neural networks, the Intel Loihi test chip uses many fewer resources on the same task.

Further, it is up to 1,000 times more energy-efficient than general purpose computing required for typical training systems.

In the first half of 2018, the Intel Loihi test chip will be shared with university and research institutions with a focus on advancing AI.

The Loihi test chip's features include:

  • 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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