And we’re on!! Come and experience how you can inject industry-leading AI inference in your computer vision solutions @embedded_world , Hall 2-440 Haven’t you scheduled your meeting yet? Don’t miss out! https://
tinyurl.com/27la66yl
COMPUTING
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Industry-Leading AI Inference for Computer Vision Solutions
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Language Limits: Speaking About Non-Computable Concepts
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I literally don't know how to say anything about non computable things, because it would break my language
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Neural operators accelerate simulations and design
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Our @NatRevPhys perspective article on neural operators and their ability to accelerate simulations and design is now out. https://
rdcu.be/dD8BI @Nature 1. Neural operators learn mappings between functions, e.g. spatiotemporal processes and partial differential equations. -
Forward/Backward Implementation Complete, Now Optimizing with CUDA
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Once you have the forward/backward, the rest of it (data loader, Adam update, etc) are mostly trivial. The real fun starts now though: I am now porting this to CUDA layer by layer so that it can be made efficient, perhaps even coming within reasonable fraction of PyTorch, but
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Memory Allocation Strategy in LLM Training Implementation
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You can look at the raw training implementation here: https://
github.com/karpathy/llm.c
/blob/master/train_gpt2.c
… You'll see that we allocate all the required memory a single time in the beginning in one large block of 1D memory. From there on during training, no memory gets created or destroyed, so we stay at -
Implementing Neural Network Layers with Memory Pointer Management
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Once you have all the layers, you just string all it all together. Not gonna lie, this was quite tedious and masochistic to write because you have to make sure all the pointers and tensor offsets are correctly arranged. Left: we allocate a single 1D array of memory and then
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llm.c: Train GPT-2 in Pure C Without Heavy Dependencies
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Have you ever wanted to train LLMs in pure C without 245MB of PyTorch and 107MB of cPython? No? Well now you can! With llm.c: https://
github.com/karpathy/llm.c To start, implements GPT-2 training on CPU/fp32 in only ~1,000 lines of clean code. It compiles and runs instantly, and exactly -
AI Processor Architecture Delivers Energy Efficiency at Scale
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Thrilled to be @EPTmagazine
's April 2024 cover story! Driven by our customer's success & industry standards, our at-memory compute architecture delivers unparalleled energy efficiency for #AI inference at scale. Read the featured article out now. https://
ept.ca/features/untet
hering-the-development-of-processors-with-ai/
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GPU Computing: Mining Crypto versus Building AI Intelligence
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why waste your GPUs mining bitcoin when you can literally turn your compute into intelligence
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Cohere Showcases Generative AI Models on Google Cloud TPU
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Cohere is excited to be at @googlecloud Next '24. On April 9, from 6:00 – 6:30 pm PDT, Dwarak Talupuru (
@DwaraknathG
), ML Engineer, will showcase how we harness the power of Google Cloud TPU for groundbreaking Generative AI models. Don't miss this glimpse into the future of AI!