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- Hacker News
- > So could neuromorphic processing be exactly that or is it not mature yet?
So, take this with a grain of salt. I only dabbled in Computational Neuroscience and met some people working on neuromorphic computing, but I am not an expert.
So, the short answer is, that it is not mature yet, and there is currently no indication that this will change in the foreseeable future. Neuromorphic chips are built for spiking neural networks, not the matrix multiplications behind LLMs and CNNs. The promise are huge improvements in energy efficiency for edge stuff like sensors and robotics, but SNNs still can't match standard deep learning on the benchmarks that matter. The software ecosystem is nowhere near PyTorch/CUDA, there was no AlexNet moment for SNNs, hardware access is limited to a few research groups, and funding follows from that. So, as you can see, it is a bit circular. If you are interested in neuromorphic computing, have a look at SpiNNaker2
- Thanks for the comment. Even if they don't compete with traditional LLMs and CNNs, do the people you spoke with see any potential breakthrough application of this type of chip?by Gooblebrai
- No, it won't. The problem is that the efficiencies of analog multiplications get swamped by the need to digitize the outputs at some point. Converting back to digital with many bits of precision is a slow, energy consuming process.
If you want to take a single analog voltage, and convert it to 16 bits, for example, at 80 million samples per second, the LTC2163[1,2] will do it, but it's a $120 chip, and consumes 108 mWatt to do it. Lower resolutions, and slower speeds cost less, but still that's the major problem you encounter.
[1] https://www.digikey.com/en/products/detail/analog-devices-in...
[2] https://www.analog.com/media/en/technical-documentation/data...
by mikewarot