I am not sure whether this was posted before.
The Case for Neuromorphic Computing today. Big Tech is investing more than $364B per year in data centres. Some mega-projects consume gigawatts of power, comparable to entire nuclear plants. Energy today ⢠Image generation is about 2.9 Wh per image, roughly half a phone charge ⢠Training...
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Full paper can be found here:
The Case for Neuromorphic Computing today.
Big Tech is investing more than $364B per year in data centres. Some mega-projects consume gigawatts of power, comparable to entire nuclear plants.
Energy today
⢠Image generation is about 2.9 Wh per image, roughly half a phone charge
⢠Training GPT-3 consumed about 1.3 GWh
⢠GPT-4-class models are estimated at tens of GWh
The cost is enormous, in grid demand, and in environmental impact.
Neuromorphic computing provides a different path. These brain-inspired chips process information using spikes, events, and in-memory computation instead of brute-force matrix operations.
State of the art
⢠Intel Hala Point with 1.15 billion neurons for research
⢠IBM NorthPole delivering up to 25 times more energy efficiency
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BrainChip Akida running edge AI in the sub-watt range
⢠SpiNNaker 2 and BrainScaleS enabling fast and frugal neural simulation
Current systems already show 10 to 25 times energy savings on relevant AI workloads.
The future is not about abandoning GPUs. It is about hybrid models where neuromorphic hardware manages sparse and event-driven tasks while GPUs handle dense training.
For AI to scale responsibly, we must focus not only on bigger compute but also on smarter compute.
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