Second-generation research chip uses
pre-production Intel 4 process and grows to 1 million neurons.
Intel adds open software framework to accelerate developer
innovation and path to commercialization.
What’s New: Today, 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.
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the full release here:
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A photo shows Intel’s Loihi 2
neuromorphic chip on the tip of a finger. Loihi 2 is Intel's
second-generation neuromorphic research chip. It supports new
classes of neuro-inspired algorithms and applications, while
providing faster processing, greater resource density and improved
energy efficiency. It was introduced by Intel in September 2021.
(Credit: Walden Kirsch/Intel Corporation)
“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.”
-- Mike Davies, director of Intel’s Neuromorphic Computing Lab
Why It Matters: 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.
About Loihi 2: 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 processing1, up to 15 times greater
resource density2 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."
About Key Breakthroughs: 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 accuracy3.
- 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.
More details may be found in the Loihi 2/Lava technical product
brief.
About the Intel Neuromorphic Research Community: The
Intel Neuromorphic Research Community (INRC) has grown to nearly
150 members, with several new additions this year, including Ford,
Georgia Institute of Technology, Southwest Research Institute
(SwRI) and Teledyne-FLIR. New partners join a robust community of
academic, government and industry partners that are working with
Intel to drive advances in real-world commercial usages of
neuromorphic computing. (Read what our partners are saying about
Loihi technology.)
“Advances like the new Loihi 2 chip and the Lava API are
important steps forward in neuromorphic computing,” said Edy
Liongosari, chief research scientist and managing director at
Accenture Labs. “Next-generation neuromorphic architecture will be
crucial for Accenture Labs’ research on brain-inspired computer
vision algorithms for intelligent edge computing that could power
future extended-reality headsets or intelligent mobile robots. The
new chip provides features that will make it more efficient for
hyper-dimensional computing and can enable more advanced on-chip
learning, while the Lava API provides developers with a simpler and
more streamlined interface to build neuromorphic systems.”
About the Path to Commercialization: Advancing
neuromorphic computing from laboratory research to commercially
viable technology is a three-pronged effort. It requires continual
iterative improvement of neuromorphic hardware in response to the
results of algorithmic and application research; development of a
common cross-platform software framework so developers can
benchmark, integrate, and improve on the best algorithmic ideas
from different groups; and deep collaborations across industry,
academia and governments to build a rich, productive neuromorphic
ecosystem for exploring commercial use cases that offer near-term
business value.
Today’s announcements from Intel span all these areas, putting
new tools into the hands of an expanding ecosystem of neuromorphic
researchers engaged in re-thinking computing from its foundations
to deliver breakthroughs in intelligent information processing.
What’s Next: Intel currently offers two Loihi 2 based
neuromorphic systems through the Neuromorphic Research cloud to
engaged members of the INRC: Oheo Gulch, a single chip system for
early evaluation and Kapoho Point, an eight-chip system that will
be available soon. The Lava Software Framework is available for
free download on GitHub. A presentation and tutorials on Loihi 2
and Lava will be featured at the upcoming Intel Innovation event in
October.
More Context: Neuromorphic Computing – Next Generation of
AI (Intel.com) | Neuromorphic Computing at Intel (Press
Kit)
About Intel
Intel (Nasdaq: INTC) is an industry leader, creating
world-changing technology that enables global progress and enriches
lives. Inspired by Moore’s Law, we continuously work to advance the
design and manufacturing of semiconductors to help address our
customers’ greatest challenges. By embedding intelligence in the
cloud, network, edge and every kind of computing device, we unleash
the potential of data to transform business and society for the
better. To learn more about Intel’s innovations, go to
newsroom.intel.com and intel.com.
1 Based on Lava simulations in September, 2021 of a nine-layer
variant of the PilotNet DNN inference workload implemented as a
sigma-delta neural network on Loihi 2 compared to the same network
implemented with SNN rate-coding on Loihi. The Lava performance
model for both chips is based on silicon characterization using the
Nx SDK release 1.0.0 with an Intel Xeon E5-2699 v3 CPU @ 2.30 GHz,
32GB RAM, as the host running Ubuntu version 20.04.2. Loihi results
use Nahuku-32 system ncl-ghrd-04. Loihi 2 results use Oheo Gulch
system ncl-og-04. Results may vary. 2 Based on the Loihi 2 core
size of 0.21 mm2 supporting up to 8192 neurons compared to the
Loihi core size of 0.41 mm2 supporting up to 1024 neurons. 3 Based
on measurements of the nine-layer PilotNet DNN inference workload
referenced above, with a sigma-delta neural network implementation
on Loihi 2 achieving a mean-squared error (MSE) of 0.035 with
323,815 synaptic operations compared to a rate-coded SNN on Loihi 1
achieving MSE of 0.0412 with 20,250,023 synaptic operations.
© Intel Corporation. Intel, the Intel logo and other Intel marks
are trademarks of Intel Corporation or its subsidiaries. Other
names and brands may be claimed as the property of others.
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Supriya Venkat 503-320-8024 supriya.venkat@intel.com
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