In September 2017 Intel developed a self-learning chip named as “Loihi” that was capable to mimic the functions of the brain by learning to operate from a various mode of feedback from the environment that’s why chip doesn’t need to be trained in the traditional way. This is the perfect example of neuromorphic computing which includes self-learning from its environment.
This week, Intel hosted the Neuro-Inspired Computational Elements (NICE) workshop at Oregon campus with the goal of bringing together researchers from different scientific disciplines to discuss and explore the development of next-generation computing architectures, including neuromorphic computing.
Intel has done with fabrication and packaging of Loihi and done with power-on and validations. Intel has tested the chip and emulator worked as it was expected.
Now, Intel has announced the formation of the Intel Neuromorphic Research Community (INRC) – an effort to create a network of collaborators spanning academic, government and industry research groups. The workshop attendees can submit their proposal for the research areas like
Neuromorphic Theory: Abstracting neuroscience understanding and relating it to practical computational models.
Spiking Neural Network Algorithms: Principled development of neural network dynamics, features and learning rules.
Neuromorphic Applications: Systems and software that use Loihi and future Intel neuromorphic architectures to solve real-world problems.
Programming Models: New paradigms for conceptualizing and specifying the structure and emergent behavior of neuromorphic systems.
Event-Driven Sensing and Control Technologies: Novel and efficient approaches for interfacing spike-based computing systems with real-world data and systems.
Qualifying proposals may receive grants for their work as well as access to a software development kit and a Loihi test system.
With this research, Intel can move the Loihi from prototype to industry-leading product. This will also be a big technological achievement from Intel.
Loihi has some major highlights like:
- Fully 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 14nm 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.