Temporal-coded Spiking Transformer

Spiking Neural Networks (SNNs) have garnered significant attention due to their biological plausibility and low power consumption. While spiking transformers enhance performance by combining SNNs with transformer architecture, most rely on rate coding, limiting energy efficiency. Temporal coding methods, such as Time-To-First-Spike (TTFS) coding, offer a more efficient alternative by encoding information based on the timing of a single spike. However, integrating TTFS with transformer architecture faces challenges due to incompatibility with batch normalization (BN) and residual connections (RC), which disrupt the precise spike firing times. In this paper, we propose temporal-coded BN (tBN) and temporal-coded RC (tRC) to address these issues. Building on tBN and tRC, we develop temporal-coded spiking attention (TSA) and temporal-coded spiking transformer (T-SpikeFormer), the first to combine TTFS coding with transformer architecture. Experimental results show our model achieves state-of-the-art performance for temporal-coded SNNs and comparable results to rate-coded SNNs while significantly reducing power consumption.