Reconfigurable Transformer Accelerator
Encoder and decoder accelerator with sparsity-aware data mapping and task-based precision control.
This project designs a 2-D array-based architecture for Transformer processing. The accelerator can reconfigure between encoder and decoder blocks, supports token pruning, and adapts bit precision by task in attention computations.
This work appears in ISLPED 2024.