Spiking Transformers and Event-Driven Foundation Models: Surrogate Gradient Dynamics, Spike-Driven Attention, and Neuromorphic Silicon Benchmarks
Standard digital Transformer architectures rely on dense matrix-matrix multiplications (GEMM) characterized by continuous-valued activations, generating massive thermal energy footprints and memory bandwidth bottlenecks during inference and training. In contrast, biological brains process complex spatio-temporal inform...