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2026年10月，麻省理工学院林肯实验室介绍了持续更新的人工智能计算硬件调查。团队自2018年开始整理商业加速器资料，最新一轮覆盖超过120种产品，数量明显高于最初的57种。调查把芯片、加速卡和完整系统分开比较，重点记录峰值计算性能及峰值功耗，帮助研究人员了解不同架构的能力边界。

人工智能加速器并非只有图形处理器，还包括专用芯片、可编程逻辑器件和数据流架构等，各自在灵活性、能效与适用任务上有所不同。相关硬件除用于神经网络，也可加速分子建模和流体模拟。最新研究进一步分析处理器核心数量、并行能力等设计选择如何影响系统表现。团队使用公开资料建立数据集，并持续跟踪企业发布和技术论文。由于部分厂商没有公开完整参数，峰值指标也不等于具体业务中的实际速度，这份调查更适合作为硬件筛选与研究的起点，实际选型仍应结合目标任务测试。

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好好 学习了 确实不错