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Comment by sabalaba | Hacker News Reader

Software. Software. Software. Just two companies, Google and NVIDIA, have publicly launched a viable service or software stack. Just two companies have successfully written a "sufficiently advanced compiler". Just two companies actually have a product. And Google refuses to step into the arena and actually compete with NVIDIA. Man, what a time we live in.

And no, AMD doesn't count. ROCm is a mess.

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Disclaimer: am a co-founder of this company

Applied Brain Research has software called Nengo (www.nengo.ai) explicitly for developing neural network models and compiling them to different backends, including CPUs, GPUs, and neuromorphic hardware (Intel's Loihi, Spinnaker, Spinnaker 2, BrainDrop). It's been battle tested for over 10 years of developing models, built the world's largest functional brain model (https://bit.ly/2VNGgSX), integrates deep learning and spiking neural networks. Would be interested to hear your thoughts on it.

I wonder if WebGPU will reduce dependence on CUDA, esp as Tensorflow is being ported to WebGPU. With WebGPU's improved performance and utility and the fact that it runs on top of Vulcan, Metal and D3D with any GPU that has drivers for those, I wonder if DL folks will find it more tempting to use TFJS/WebGPU via Electron or the browser and just be done with CUDA (i.e. break or soften NVIDIA's monopoly)

I've never heard anyone suggest that WebGPU would be appropriate for ML training workloads. Maybe inference, but not training.

Well, the memory limit of a WebGPU process would be the limiting factor for training. In addition, the bandwidth between the nodes and the parameter server, if doing training in data-parallel fashion, is another limiting factor.

There is one more company with a viable compiler: https://ai.facebook.com/tools/glow/

I don't have first hand experience using it, but from people I know, it does work.

Is there a good summary of the state of art in this field of software infra for ML?

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