11/21/2024, 09:50 AM UTC
NPU将面临洗牌：加速AI和NPU生态系统的复杂性NPU Shake-Up Ahead: The Complexity of Accelerating AI and the NPU Ecosystem
<p>➀ 边缘AI的兴起促使半导体设计师构建加速器以提高性能和低功耗，导致内部、初创公司和商业IP产品组合中NPUs的激增。</p><p>➁ 围绕神经网络架构、AI模型和基础模型的软件和硬件复杂性正在爆炸式增长，需要复杂的软件编译器和指令集模拟器。</p><p>➂ 推理平台的硬件复杂性正在演变，尤其关注性能和功耗效率，特别是在边缘应用中。</p><p>➃ 将张量引擎、矢量引擎和标量引擎组合在一起以解决加速挑战，在多个集群中是复杂且昂贵的。</p><p>➄ NPUs的供应链和生态系统正变得越来越复杂，中间制造商和软件公司资源有限，难以支持广泛的平台。</p><p>➀ The rise of edge AI has spurred semiconductor designers to build accelerators for performance and low power, leading to a proliferation of NPUs among in-house, startup, and commercial IP product portfolios.</p><p>➁ The complexity of software and hardware around neural network architectures, AI models, and base models is exploding, requiring sophisticated software compilers and instruction set simulators.</p><p>➂ The hardware complexity of inference platforms is evolving, with a focus on performance and power efficiency, especially for edge applications.</p><p>➃ The combination of tensor engines, vector engines, and scalar engines in multiple clusters to address the challenges of acceleration is complex and costly.</p><p>➄ The supply chain and ecosystem for NPUs are becoming increasingly complex, with intermediate manufacturers and software companies having limited resources to support a wide range of platforms.</p>
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