Intel Neuropmorphic Learning Lohi 2

Intel’s Neuromorphic Research Chip Loihi Demonstrates Real-Time Learning With 175x Lower Energy.

Intel Labs Improves Interactive, Continual Learning for Robots with Neuromorphic Computing.

Intel Labs, in collaboration with the Italian Institute of Technology and the Technical University of Munich, has introduced a new approach to neural network-based object learning. It specifically targets future applications like robotic assistants that interact with unconstrained environments, including in logistics, healthcare or elderly care. This research is a crucial step in improving the capabilities of future assistive or manufacturing robots. It uses neuromorphic computing through new interactive online object learning methods to enable robots to learn new objects after deployment.

iCub Robot
The iCub Robot Developed At The Italian Institute of Technology models human learning and development in a real world setting. Intel Labs’s Neuromorphic Computing Lab Studies Spiking Neuronal Architectures In Neuromorphic Chips In These Kind of Real World Settings to Develop With A More Agile, Adaptive Brain Capable Of Learning And Interacting With A User ( Image from Intel Corporation)

Using these new models, Intel and its collaborators successfully demonstrated continual interactive learning on Intel’s neuromorphic research chip, Loihi, measuring up to 175x lower energy to learn a new object instance with similar or better speed and accuracy compared to conventional methods running on a central processing unit (CPU). To accomplish this, researchers implemented a spiking neural network architecture on Loihi that localized learning to a single layer of plastic synapses and accounted for different object views by recruiting new neurons on demand. This enabled the learning process to unfold autonomously while interacting with the user.

iCub Simulated Setup
In a Simulated Setup, A Robot Actively Senses Objects By Moving Its Eyes (Event-Based Camera or Dynamic Vision Sensor) Generating “miscrosaccades” The Events Collected Are Used To Drive A Spiking Neural Network On The Lohi Chip. If The Object Or View Is New, Its SNN Representation Is Learned Or Updated. If The Object Is Known, It Is Recognized By The Network, And Respective Feedback Is Given To The User. (Credit: Intel Corporation)

The research was published in the paper “Interactive continual learning for robots: a neuromorphic approach,” which was named “Best Paper” at this year’s International Conference on Neuromorphic Systems (ICONS) hosted by Oak Ridge National Laboratory. 

“When a human learns a new object, they take a look, turn it around, ask what it is, and then they’re able to recognize it again in all kinds of settings and conditions instantaneously,” said Yulia Sandamirskaya, robotics research lead in Intel’s neuromorphic computing lab and senior author of the paper. “Our goal is to apply similar capabilities to future robots that work in interactive settings, enabling them to adapt to the unforeseen and work more naturally alongside humans. Our results with Loihi reinforce the value of neuromorphic computing for the future of robotics.”

  • Intel Lohi Learning

Loihi 2: A New Generation of Neuromorphic Computing

Intel Labs’ second-generation neuromorphic research chip, codenamed Loihi 2, and Lava, an open-source software framework, will drive innovation and adoption of neuromorphic computing solutions.
Enhancements include:

  • Up to 10x faster processing capability1
  • Up to 60x more inter-chip bandwidth2
  • Up to 1 million neurons with 15x greater resource density3
  • 3D Scalable with native Ethernet support
  • A new, open-source software framework called Lava
  • Fully programmable neuron models with graded spikes
  • Enhanced learning and adaptation capabilities

Intel Labs is leading research efforts to help realize neuromorphic computing’s goal of enabling next-generation intelligent devices and autonomous systems. Guided by the principles of biological neural computation, neuromorphic computing uses new algorithmic approaches that emulate how the human brain interacts with the world to deliver capabilities closer to human cognition.
Spiking neural networks (SNNs), novel models that simulate natural learning by dynamically re-mapping neural networks, are used in neuromorphic computing to make decisions in response to learned patterns over time. Neuromorphic processors leverage these asynchronous, event-based SNNs to achieve orders of magnitude gains in power and performance over conventional architectures.

Neuromorphic computing’s innovative architectural approach will power future autonomous AI solutions that require energy efficiency and continuous learning. It promises to open exciting new possibilities in computing and is already in use in a variety of areas including, sensing, robotics, healthcare, and large-scale AI applications.

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