The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean

📋 论文概要

本文作为ISSCC 2020的keynote配套论文，回顾了深度学习在计算机视觉、语音识别等领域取得的突破，并探讨了这些进展对计算机架构和芯片设计的启示，强调了硬件与软件协同设计的重要性。

💡 主要创新点

• 提出了深度学习对专用硬件加速器（如TPU）的需求，并展望了未来芯片设计方向。
• 强调了模拟计算、内存计算等新型计算范式在深度学习硬件中的潜力。
• 倡导硬件-软件协同设计，以适应不断演进的神经网络模型。

🏷 关键词

📄 原文摘要

The past decade has seen a remarkable series of advances in machine learning, and in particular deeplearning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the postMoore’s Lawera. It also discusses some of the ways that machine learning may be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, exampleand

👥 作者与机构

Google Research, Mountain View, CA, Abstract