The role of cache in AI processor design
By Frank Schirrmeister, Arteris
EDN (March 22, 2024)
Artificial intelligence (AI) is making its presence felt everywhere these days, from the data centers at the Internet’s core to sensors and handheld devices like smartphones at the Internet’s edge and every point in between, such as autonomous robots and vehicles. For the purposes of this article, we recognize the term AI to embrace machine learning and deep learning.
There are two main aspects to AI: training, which is predominantly performed in data centers, and inferencing, which may be performed anywhere from the cloud down to the humblest AI-equipped sensor.
AI is a greedy consumer of two things: computational processing power and data. In the case of processing power, OpenAI, the creator of ChatGPT, published the report AI and Compute, showing that since 2012, the amount of compute used in large AI training runs has doubled every 3.4 months with no indication of slowing down.
With respect to memory, a large generative AI (GenAI) model like ChatGPT-4 may have more than a trillion parameters, all of which need to be easily accessible in a way that allows to handle numerous requests simultaneously. In addition, one needs to consider the vast amounts of data that need to be streamed and processed.
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