A training-free thresholding algorithm raises activation sparsity in RWKV recurrent LLMs to 57-63%, yielding a simulated 1.9x energy/latency gain on SENECA with a small accuracy loss.
Rwkv: Reinventing rnns for the transformer era,
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Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing
A training-free thresholding algorithm raises activation sparsity in RWKV recurrent LLMs to 57-63%, yielding a simulated 1.9x energy/latency gain on SENECA with a small accuracy loss.