Bravo
If ARM was an arm, BRN would be its biceps💪!
I just stumbled over this paper on Reddit.
6 mo. ago
Singularian2501
Paper: https://arxiv.org/abs/2404.03325
In my opinion, neuromorphic computing is the future as it is far more power efficient than current GPUs that are only optimized for graphics. I think we need an NPU = neuromorphic processing unit in addition to the GPU. I also found it very important that models like gpt-4 (MLLM) can be copied and loaded from it, otherwise they become as useless as the TrueNorth chip, which cannot load models like gpt-4 https://en.wikipedia.org/wiki/Cognitive_computer#IBM_TrueNorth_chip . Spiking neural networks (SNN) are also far more energy efficient. They are the future of AI and especially robotics and MLLM inference. Deepmind - Mixture-of-Depths: Dynamically Allocation Compute in Transformer-based Language Models Paper: https://arxiv.org/abs/2404.02258 show that the field must evolve towards biologically plausible SNN architectures and specialized neuromorphic computing chips that come with them. Because here the transformer is much more like a biological neuron that is only activated when it is needed. Either Nvidia or another chip company needs to develop the hardware and software stack that allows easy training of MLLM like gpt-4 with SNN running on neuromorphic hardware. In my opinion, this should enable 10,000x faster inference speeds while using 10,000x less energy, allowing MLLMs to run locally on robots, PCs and smartphones.
Abstract:
https://www.reddit.com/r/mlscaling/comments/1bxci53/embodied_neuromorphic_artificial_intelligence_for/
6 mo. ago
Singularian2501
Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack - New York University 2024 - Highly important to make inference much much faster and allows if scaled in the hard and software stack running gpt-4 locally on humanoid robots!
Paper: https://arxiv.org/abs/2404.03325
In my opinion, neuromorphic computing is the future as it is far more power efficient than current GPUs that are only optimized for graphics. I think we need an NPU = neuromorphic processing unit in addition to the GPU. I also found it very important that models like gpt-4 (MLLM) can be copied and loaded from it, otherwise they become as useless as the TrueNorth chip, which cannot load models like gpt-4 https://en.wikipedia.org/wiki/Cognitive_computer#IBM_TrueNorth_chip . Spiking neural networks (SNN) are also far more energy efficient. They are the future of AI and especially robotics and MLLM inference. Deepmind - Mixture-of-Depths: Dynamically Allocation Compute in Transformer-based Language Models Paper: https://arxiv.org/abs/2404.02258 show that the field must evolve towards biologically plausible SNN architectures and specialized neuromorphic computing chips that come with them. Because here the transformer is much more like a biological neuron that is only activated when it is needed. Either Nvidia or another chip company needs to develop the hardware and software stack that allows easy training of MLLM like gpt-4 with SNN running on neuromorphic hardware. In my opinion, this should enable 10,000x faster inference speeds while using 10,000x less energy, allowing MLLMs to run locally on robots, PCs and smartphones.
Abstract:
https://www.reddit.com/r/mlscaling/comments/1bxci53/embodied_neuromorphic_artificial_intelligence_for/
) was an adolescent and had diarrhea..
