20.5 C-Transformer: A 2.6-18.1Μj/token Homogeneous DNN-Transformer/Spiking-Transformer Processor with Big-Little Network and Implicit Weight Generation for Large Language Models
2024 IEEE International Solid-State Circuits Conference (ISSCC)（2024）
Key words
Language Model,Large Language Models,Energy Consumption,Power Consumption,Sparsity,Input Values,Random Values,Question Answering,Input Channels,Spiking Neural Networks,Perplexity,Least Significant Bit,Real-time Response,Increase Energy Efficiency,Datapath,Position Embedding,Output Spike,Area Overhead
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