A hardware-software co-design for spiking transformers that bundles tokens over time, routes sparse and dense work to different cores, and error-bounded prunes attention, achieving about 6x speedup over prior spiking accelerators in simulation.
Transformer-xl: Attentive language models beyond a fixed-length context,
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Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning
A hardware-software co-design for spiking transformers that bundles tokens over time, routes sparse and dense work to different cores, and error-bounded prunes attention, achieving about 6x speedup over prior spiking accelerators in simulation.