Contrastive learning and a unified ordinal output let a transformer predict optimal accelerator hardware configurations for DNN workloads at constant time, with 91% accuracy on a MAESTRO-based dataset.
Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable interconnects,
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AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations
Contrastive learning and a unified ordinal output let a transformer predict optimal accelerator hardware configurations for DNN workloads at constant time, with 91% accuracy on a MAESTRO-based dataset.