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An idle AI chip is an expensive one. Here is how batching and scheduling keep accelerators busy, and the tradeoffs between speed and efficiency.
Field-programmable gate arrays can be rewired after they are made. Here is how FPGAs work, where they fit in AI and how they compare with GPUs...
Before a chip is manufactured, it goes through years of design, simulation and verification. Here is a walk-through of the chip design process.
Chips from the same wafer can perform differently, and some do not work at all. Here is how yield and binning work, and why they shape...
AI chips include specialized hardware built only for matrix math. Here is how tensor-core-style units and systolic arrays work in general terms.
Large AI models are too big for any one chip, so accelerators must share data constantly. Here is how interconnects work and why they matter so...
Transistors have changed shape to keep shrinking. Here is how flat designs gave way to fins and then to fully wrapped gates, and why each shift...
Moore's Law gets the fame, but Dennard scaling explained why shrinking transistors once made chips both faster and cooler. Its end changed chip design.
Each new generation of AI accelerator tends to draw more power than the last. Here is why that happens and what it means for servers and...
Not all AI runs in giant data centers. Edge AI chips run models on phones, cars, cameras and factory equipment. Here is how they differ and...
Quantization stores a model's numbers with fewer bits so it uses less memory and power. Here is how it works and what tradeoffs it brings.
AI chips can often compute far faster than they can fetch data. This gap, known as the memory wall, shapes chip design more than most headlines...
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