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> At what point does it become more feasible to rewrite your architecture or use less GPU They are but it takes a lot of time. Most of the big players - Googl
by declaredapple 3y ago
> At what point does it become more feasible to rewrite your architecture or use less GPU
They are but it takes a lot of time.
Most of the big players - Google, Meta, OpenAI, Amazon, and Microsoft all are actively developing TPU/NPUs that would be used instead of the H100/A100's everyone is using for machine learning.
Google (tensorflow/jax), Meta(pytorch), Microsoft(onyx), Openai(triton) and Apple (mlx) each have software stacks for optimizing models for multiple platforms.
It takes a lot of time to develop the silicon and software stack. As a result everyone is using H100's in the interum until the hardware/software catches up. Google has been using their TPUs already.
There's other companies like Groq that are also developing NPU/TPU like devices.
- altruios 3y agoIs there an Open (source) TPU project?
- nl 3y agoThere is SIMT on RISC V: https://www.semanticscholar.org/paper/Simty-%3A-generalized-SIMT-execution-on-RISC-V-Inria-Collange/6ca0d660157ba938ef6bb318b2207626bb32a5d6 https://www.semanticscholar.org/paper/Simty-%3A-generalized-... And some PoC work: https://vortex.cc.gatech.edu/publications/hotchips-poster.pdf https://vortex.cc.gatech.edu/publications/hotchips-poster.pd... Neither are specialized TPU/NPUs but they do fast vector operations.