Core Insights - The article discusses the evolution of algorithms in the chip design industry, particularly focusing on the advancements in attention mechanisms and their implications for future chip designs [2][4]. Group 1: Attention Mechanism Evolution - The Transformer architecture has dominated the large model field, but its self-attention mechanism poses significant computational challenges, especially in terms of power requirements during the prefill and decode phases [4]. - Various improvements to the Transformer structure have been proposed, such as Performer, Reformer, and lnformer, but none have achieved widespread application due to a lack of strong demand [4]. - The emergence of linear attention mechanisms aims to reduce computational complexity to linear levels, with models like RWKV and Mamba following this approach [5]. Group 2: Dynamic Sparsity and MoE Technology - Dynamic sparsity, particularly through Mixture of Experts (MoE) technology, has gained traction, allowing only a subset of experts to be activated during inference, which can lead to better performance and reduced computational costs [8]. - The trend towards increased sparsity in MoE models, such as Ant Group's recent models, indicates a significant shift in the industry, necessitating larger memory and bandwidth requirements [9]. Group 3: Low-Bit Quantization - The introduction of low-bit quantization techniques, such as FP8 training, has opened new avenues for model efficiency, with a focus on weight-only quantization to alleviate bandwidth bottlenecks [11]. - The article highlights the importance of fine-grained quantization and the potential for mixed quantization strategies to optimize model performance, especially in MoE models [12]. Group 4: Token Compression - Token compression has emerged as a critical area for reducing the computational burden of large models, particularly in visual token processing, which has shown high redundancy [14]. - The article notes a surge in research focused on token compression techniques, which could significantly impact chip design by lowering application barriers for large models [14]. Group 5: Future Implications for Chip Design - The advancements in attention mechanisms, dynamic sparsity, low-bit quantization, and token compression are expected to have substantial implications for the design of future edge chips, which have lagged behind the development of large models [14].