Dr Michael Mayberry describes the launch of Intel’s first-of-its-kind self-learning chip codenamed Loihi.
Chips and machine intelligence
Imagine a future where complex decisions could be made faster and adapt over time. Where societal and industrial problems can be autonomously solved using learned experiences.
It’s a future where first responders using image-recognition applications can analyze streetlight camera images and quickly solve missing or abducted person reports.
It’s a future where stoplights automatically adjust their timing to sync with the flow of traffic, reducing gridlock and optimizing starts and stops.
It’s a future where robots are more autonomous and performance efficiency is dramatically increased.
An increasing need for collection, analysis and decision-making from highly dynamic and unstructured natural data is driving demand for compute that may outpace both classic CPU and GPU architectures. To keep pace with the evolution of technology and to drive computing beyond PCs and servers, Intel has been working for the past six years on specialized architectures that can accelerate classic compute platforms. Intel has also recently advanced investments and R&D in artificial intelligence (AI) and neuromorphic computing.
Our work in neuromorphic computing builds on decades of research and collaboration that started with CalTech professor Carver Mead, who was known for his foundational work in semiconductor design. The combination of chip expertise, physics and biology yielded an environment for new ideas. The ideas were simple but revolutionary: comparing machines with the human brain. The field of study continues to be highly collaborative and supportive of furthering the science.
As part of an effort within Intel Labs, Intel has developed a first-of-its-kind self-learning neuromorphic chip – codenamed Loihi – that mimics how the brain functions by learning to operate based on various modes of feedback from the environment. This extremely energy-efficient chip, which uses the data to learn and make inferences, gets smarter over time and does not need to be trained in the traditional way. It takes a novel approach to computing via asynchronous spiking.
We believe AI is in its infancy and more architectures and methods – like Loihi – will continue emerging that raise the bar for AI. Neuromorphic computing draws inspiration from our current understanding of the brain’s architecture and its associated computations. The brain’s neural networks relay information with pulses or spikes, modulate the synaptic strengths or weight of the interconnections based on timing of these spikes, and store these changes locally at the interconnections. Intelligent behaviors emerge from the cooperative and competitive interactions between multiple regions within the brain’s neural networks and its environment.
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