Intel Advances Neuromorphic with Loihi 2


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Intel introduced Loihi 2, its second-generation neuromorphic research chip, and Lava, an open-source software framework for developing neuro-inspired applications. Their introduction signals Intel’s ongoing progress in advancing neuromorphic technology.

“Loihi 2 and Lava harvest insights from several years of collaborative research using Loihi. Our second-generation chip greatly improves the speed, programmability, and capacity of neuromorphic processing, broadening its usages in power and latency constrained intelligent computing applications. We are open sourcing Lava to address the need for software convergence, benchmarking, and cross-platform collaboration in the field, and to accelerate our progress toward commercial viability,” said Mike Davies, director of Intel’s Neuromorphic Computing Lab.

Neuromorphic computing, which draws insights from neuroscience to create chips that function more like the biological brain, aspires to deliver orders of magnitude improvements in energy efficiency, speed of computation and efficiency of learning across a range of edge applications: from vision, voice and gesture recognition to search retrieval, robotics, and constrained optimization problems.

Applications Intel and its partners have demonstrated to date include robotic arms, neuromorphic skins and olfactory sensing.

The research chip incorporates learnings from three years of use with the first-generation research chip and leverages progress in Intel’s process technology and asynchronous design methods.

Advances in Loihi 2 allow the architecture to support new classes of neuro-inspired algorithms and applications, while providing up to 10 times faster processing, up to 15 times greater resource density with up to 1 million neurons per chip, and improved energy efficiency. Benefitting from a close collaboration with Intel’s Technology Development Group, Loihi 2 has been fabricated with a pre-production version of the Intel 4 process, which underscores the health and progress of Intel 4. The use of extreme ultraviolet (EUV) lithography in Intel 4 has simplified the layout design rules compared to past process technologies. This has made it possible to rapidly develop Loihi 2.

The Lava software framework addresses the need for a common software framework in the neuromorphic research community. As an open, modular, and extensible framework, Lava will allow researchers and application developers to build on each other’s progress and converge on a common set of tools, methods, and libraries. Lava runs seamlessly on heterogeneous architectures across conventional and neuromorphic processors, enabling cross-platform execution and interoperability with a variety of artificial intelligence, neuromorphic and robotics frameworks. Developers can begin building neuromorphic applications without access to specialized neuromorphic hardware and can contribute to the Lava code base, including porting it to run on other platforms.

"Investigators at Los Alamos National Laboratory have been using the Loihi neuromorphic platform to investigate the trade-offs between quantum and neuromorphic computing, as well as implementing learning processes on-chip,” said Dr. Gerd J. Kunde, staff scientist, Los Alamos National Laboratory. “This research has shown some exciting equivalences between spiking neural networks and quantum annealing approaches for solving hard optimization problems. We have also demonstrated that the backpropagation algorithm, a foundational building block for training neural networks and previously believed not to be implementable on neuromorphic architectures, can be realized efficiently on Loihi. Our team is excited to continue this research with the second generation Loihi 2 chip."

Loihi 2 and Lava provide tools for researchers to develop and characterize new neuro-inspired applications for real-time processing, problem-solving, adaptation and learning. Notable highlights include: 

  • Faster and more general optimization: Loihi 2’s greater programmability will allow a wider class of difficult optimization problems to be supported, including real-time optimization, planning, and decision-making from edge to datacenter systems.
  • New approaches for continual and associative learning: Loihi 2 improves support for advanced learning methods, including variations of backpropagation, the workhorse algorithm of deep learning. This expands the scope of adaptation and data efficient learning algorithms that can be supported by low-power form factors operating in online settings.
  • Novel neural networks trainable by deep learning: Fully programmable neuron models and generalized spike messaging in Loihi 2 open the door to a wide range of new neural network models that can be trained in deep learning. Early evaluations suggest reductions of over 60 times fewer ops per inference on Loihi 2 compared to standard deep networks running on the original Loihi without loss in accuracy.
  • Seamless integration with real-world robotics systems, conventional processors, and novel sensors: Loihi 2 addresses a practical limitation of Loihi by incorporating faster, more flexible, and more standard input/output interfaces. Loihi 2 chips will support Ethernet interfaces, glueless integration with a wider range of event-based vision sensors, and larger meshed networks of Loihi 2 chips.

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