AFAIK GPUs are ok at matrix math, it's just they are typically built for higher precision. AI chips probably have some other advantages but I'm not the best per

AFAIK GPUs are ok at matrix math, it's just they are typically built for higher precision. AI chips probably have some other advantages but I'm not the best person to speak to them. Here's my simplified understanding: CPUs are optimized for taking a decent amount of data and doing anything with them, especially if the order counts as is often the case with algorithms. GPUs are optimized for taking a lot of data and doing the same few things to a lot of them (shaders, physics). AI chips are optimized at taking a massive amount of data and doing a large number of calculations on all of them. In a neural net each "neuron" and/or "synapse" can represent a function/weight that needs to be calculated across many inputs. In a deep learning set, this could be millions of weights and the more weights you have the more important memory and power efficiency become. When a NN is being looked up into (as opposed to the initial training) it's often in real time (self driving cars, "hey siri", etc.) so the demand to finish all those calculation's faster is even higher. OTOH the accuracy is less important, as neural nets - like people - are good at filtering out noise.