In modern sequence modeling, the challenge of compressing context into a smaller state is paramount. By reframing models like attention, we see how it fundamentally fails at this, making autoregressive inference inefficient by necessitating storage of the entire context. Our method introduces a selection mechanism to mitigate this, achieving better modeling while improving computational efficiency.
The proposed selective state space models (SSMs) offer a novel approach to sequence modeling, moving beyond traditional architectures. By creating a simplified structure that eliminates both attention and multi-layer perceptron (MLP) blocks, we allow for significant advancements in efficiency without sacrificing performance. The hardware-aware algorithm we introduce leverages memory hierarchies to enhance computational speed, especially evident with synthetic and real-world tasks.
#machine-learning #state-space-models #computational-efficiency #sequence-modeling #model-compression
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