Artificial intelligence accelerators

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In-memory computing Neural processing units (NPUs) Photonic processors Wafer-scale engines Analog AI chips Tensor processing units (TPUs) Edge AI chips Dataflow architectures

How does a compiler decide which operations stay on the NPU? What does a systolic array do that a GPU's SIMT cores cannot? How much does hardware sparsity support actually speed up a transformer? Why are GPUs so much faster than CPUs at matrix multiplication? Why do NPUs crush matrix math but stumble on unusual operators? Why is INT8 quantization almost free while INT4 costs accuracy? Benchmarking a neuromorphic chip against a phone NPU on the same task Why do peak TOPS numbers never show up in real model throughput? Why does doubling sparsity rarely double throughput? Why do TPUs need their own compiler instead of running CUDA code? Is a phone's NPU worth using over its CPU for on-device models? Why do dynamic input shapes wreck an accelerator's efficiency? How do you keep weights flowing fast enough to feed the compute units? Which is the real inference bottleneck: FLOPs or memory bandwidth?

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