Commercial applications envisaged fall into two main categories.
One, which is where SpiNNcloud is focused, is in providing a more energy efficient and higher performance platform for AI applications – including image and video analysis, speech recognition and the large-language models that power chatbots such as ChatGPT.
Another is in “edge computing” applications – where data is processed not in the cloud, but in real time on connected devices, but which operate on power constraints. Autonomous vehicles, robots, cell phones and wearable technology could all benefit.
Technical challenges, however, remain. Long regarded as a main stumbling block to the advance of neuromorphic computing generally is developing the software needed for the chips to run.
While having the hardware is one thing, it must be programmed to work, and that can require developing from scratch a totally different style of programming to that used by conventional computers.
“The potential for these devices is huge… the problem is how do you make them work,” sums up Mr Hutcheson, who predicts it will be at least a decade, if not two, before the benefits of neuromorphic computing are really felt.
There are also issues with cost. Whether they use silicon, as the commercially oriented efforts do, or other materials, creating radically new chips is expensive, notes Prof Kenyon.
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