@insideBIGDATApodcast: The Open Source Stack Unleashing a Game-Changing AI Hardware Shift

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Welcome back to the insideBIGDATA series of podcast presentations, a curated collection of topics relevant to our global audience. We bring you compelling topics of the day including: big data, data science, machine learning, AI, and deep learning. Enjoy!

For this installment, we bring you a podcast from The Data Exchange with Ben Lorica presented by Gradient Flow featuring Dylan Patel, Chief Analyst at SemiAnalysis, a boutique semiconductor research and consulting firm focused on the semiconductor supply chain from chemical inputs to fabs to design IP and strategy. This episode discusses the emerging open source software stack for PyTorch that makes it easier and more accessible to implement non-NVIDIA backends (see his recent post). Many people have long surmised that  there will be other successful accelerators besides NVIDIA GPUs and Google TPUs. Unfortunately, the companies behind new hardware accelerators do not possess enough resources to build a software stack to mimic CUDA or XLA. A natural solution is for other players to build an open source software stack that goes all the way to the accelerator instruction set. The hope is that such a software stack matures and eventually becomes a viable alternative to CUDA.

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