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

Dylan Patel on the open source software stack poised to enable more AI hardware accelerators.

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Dylan Patel is the 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. In this episode, we discuss 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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We also covered the market share of deep learning frameworks (PyTorch; TensorFlow; JAX), the latest on RISC V, and the Biden administration’s export controls for “Certain Advanced Computing and Semiconductor Manufacturing Items; Supercomputer and Semiconductor End Use” .

Interview highlights – key sections from the video version:

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[Image: Open Source Software Stack for AI Hardware, by Ben Lorica.]