$0.21{\times }$ –

$0.33{\times }$

computation energy and

$0.37{\times }$

–

$0.41{\times }$

external memory access (EMA) energy compared to the baseline. Our chip demonstrates 13.6% lower energy consumption than the previous state-of-the-art, despite having

$2.1{\times }$

more parameters. Moreover, it consumes 63.8% less energy with a similar parameter size. The C-Transformer can complete various language model tasks with <1 s latency, notably FSMT in 0.09 s and GPT-2 in 0.656 s. By combining DNN-Transformer and Spiking-Transformer architectures, the C-Transformer enhances computational energy efficiency, eliminates external memory bottlenecks, and enables language models such as GPT-2 to achieve state-of-the-art performance on mobile devices.">

C-Transformer: An Energy-Efficient Homogeneous DNN-Transformer/SNN-Transformer Processor for Large Language Models (original) (raw)

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