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Mike Davies, Intel Labs: 'We're reaching the boundaries of basic computing'
Question. What is a neuromorphic system?
Answer. It’s a computer design architecture that’s inspired by the modern understanding of how brains operate, which means that we are discarding seven, eight decades of conventional computer architecture understanding. We’re trying to understand the principles from modern neuroscience that apply to chips and systems that we can build today to create something that operates and processes information more like how a brain does.
Q. How does it work?
A. If you were to open up the system, the chips, you see differences, some of them very striking, in the sense that there’s no memory: all the computing and the processing elements in the memory are integrated together. Our Hala Point system, for example, is a three-dimensional grid of chips. It’s similar to how you open up a brain and everything is communicating to everything. A neuron will communicate across the brain to another set of neurons that is connected. In a traditional system, you have memory sitting next to a processor and the processor is reading continuously out of the memory.
Q. Is this model necessary because we have reached the boundaries of conventional computing?
A. There’s lots of progress being made in AI and in deep learning, and it’s very exciting. But it’s hard to see how these trends that we observe in the research will continue when you see their increases in computing requirements. These AI models are growing at exponential rates, far faster than the manufacturing advances that are being made. That really is reaching the limit of what basic computer architecture can provide. And also, if we look at just the power efficiency of these traditional AI chips and systems, compared to the brain, there are many orders of magnitude of difference in power efficiency. So, it’s not so much that traditional computer architectures are not capable of providing great gains in computing and AI, it’s more that we’re looking to a broader class of functionality, being able to have computers that operate more like the brain, and do so in a very efficient way.
Answer. It’s a computer design architecture that’s inspired by the modern understanding of how brains operate, which means that we are discarding seven, eight decades of conventional computer architecture understanding. We’re trying to understand the principles from modern neuroscience that apply to chips and systems that we can build today to create something that operates and processes information more like how a brain does.
Q. How does it work?
A. If you were to open up the system, the chips, you see differences, some of them very striking, in the sense that there’s no memory: all the computing and the processing elements in the memory are integrated together. Our Hala Point system, for example, is a three-dimensional grid of chips. It’s similar to how you open up a brain and everything is communicating to everything. A neuron will communicate across the brain to another set of neurons that is connected. In a traditional system, you have memory sitting next to a processor and the processor is reading continuously out of the memory.
Q. Is this model necessary because we have reached the boundaries of conventional computing?
A. There’s lots of progress being made in AI and in deep learning, and it’s very exciting. But it’s hard to see how these trends that we observe in the research will continue when you see their increases in computing requirements. These AI models are growing at exponential rates, far faster than the manufacturing advances that are being made. That really is reaching the limit of what basic computer architecture can provide. And also, if we look at just the power efficiency of these traditional AI chips and systems, compared to the brain, there are many orders of magnitude of difference in power efficiency. So, it’s not so much that traditional computer architectures are not capable of providing great gains in computing and AI, it’s more that we’re looking to a broader class of functionality, being able to have computers that operate more like the brain, and do so in a very efficient way.
https://english.elpais.com/technology/2024-05-25/mike-davies-intel-labs-were-reaching-the-boundaries-of-basic-computing.html
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