GTAC applies a generative Transformer with irredundant encoding and self-evolutionary training to produce approximate circuits that cut delay by 30.9% and gate count by 50.5% versus baselines while using far less memory.
Late breaking results: Leveraging ap- proximate computing for carbon-aware dnn accelerators
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GTAC: A Generative Transformer for Approximate Circuits
GTAC applies a generative Transformer with irredundant encoding and self-evolutionary training to produce approximate circuits that cut delay by 30.9% and gate count by 50.5% versus baselines while using far less memory.