AI BriefingRWKV: A Combination of Transformer and RNN
Key point
RWKV, a new architecture that combines the efficiency of Transformer with the advantages of RNN, has been released.
Details
RWKV is a new architecture that combines the efficiency of RNN (Recurrent Neural Network) with the performance of Transformer, and has been integrated into the Hugging Face transformers library.
Existing RNN processes data based on the previous state, which makes it computationally efficient, but it has limitations in learning long-term dependencies due to the vanishing gradient problem. Transformer, on the other hand, solves this through self-attention and enables parallel processing, but has the drawback of high computational complexity depending on sequence length.
RWKV is designed to simultaneously provide RNN's linear computation method along with Transformer's strong performance and scalability.
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