Intel’s newest Loihi 2 neuromorphic chip allows the company to “draw insights from neuroscience to create chips that function more like the biological brain.” The effort will help to drastically improve energy efficiency on a much higher level, as well as accelerating computative learning efficiently across several edge applications, such as “vision, voice and gesture recognition to search retrieval, robotics, and constrained optimization problems.” These can be found in technologies such as neuromorphic skin, robotic arms, and olfactory sensing.
Loihi 2 utilizes several years of experience of use with the first-generation release of its predecessor, thus allowing Intel’s process technologies and asynchronous design structure to advance further into the future.
- 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.
Dr. Gerd J. Kunde, a staff scientist at the Los Alamos National Laboratory, states, “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. 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.”
Presently, Intel offers two separate Loihi 2 based neuromorphic systems through their Neuromorphic Research cloud at the Intel Neuromorphic Research Community (INRC). The first is Oheo Gulch, “a single-chip system for early evaluation.” The second is Kapoho Point, “an eight-chip system that will be available soon.” The Lava API is currently available for free download on GitHub. Presentation and tutorials on Loihi 2 and the Lava API will be showcased at Intel’s Innovation event this October.
Loihi 2 and Lava API will provide researchers the tools to create and characterize new applications in neurotechnology for problem-solving, learning, adapting solutions, as well as processing formulas in real-time.
- 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. 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.
- 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.
For more information about Loihi 2 neuromorphic processing chips and the Lava API, this technical brief about the new advancements with Loihi 2 and Lava API will explain in more detail.