传统NPU供应商，碰壁了！

Core Viewpoint - The article discusses the challenges faced by traditional and emerging companies in the NPU (Neural Processing Unit) market, emphasizing the need for a more integrated approach to matrix and general computing rather than relying on separate engines [1][4]. Group 1: Market Dynamics - The NPU IP licensing market is crowded with competitors offering various solutions, with many traditional CPU, DSP, and GPU IP providers entering the NPU accelerator space to maintain competitiveness [1][2]. - Leading IP companies have created similar AI subsystems that combine traditional cores with hardwired accelerators, resulting in a lack of differentiation in their offerings [2][4]. Group 2: Architectural Limitations - The existing architectures require algorithm partitioning to run on two engines, which works well for a limited number of algorithms but struggles with newer models like Transformers that require a broader set of graph operators [4][5]. - Traditional IP companies opted for short-term solutions by integrating matrix accelerators with existing processors, which has led to a technological trap as they now face the need for more advanced solutions [4][5]. Group 3: Long-term Challenges - The shift towards a programmable NPU capable of handling a wide range of graph operators is necessary but requires significant investment and time, which traditional companies have been reluctant to commit to [5]. - The "innovator's dilemma" is highlighted, where traditional companies must reconcile the need for new architectures with the legacy value of their existing IP cores, leading to a cycle of outdated solutions [5].