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TESSERA: A Workload-Driven Simulation and Design-Space Exploration Framework for Heterogeneous NPUs
AI model architectures are diversifying rapidly. While CNNs and transformers rely on dense matrix multiplication, emerging architectures (state-space models, fast Fourier transform (FFT)-based long convolutions, Kolmogorov-Arnold networks, and spiking networks) are not multiply-accumulate (MAC) domi
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