IBM NorthPole: An Architecture for Neural Network Inference with a 12nm Chip

📋 论文概要

本文介绍了IBM NorthPole芯片，一种专为神经网络推理设计的12nm架构。该芯片通过创新的存算一体设计和高效的片上网络，实现了高能效和低延迟的推理计算。

💡 主要创新点

• 提出了一种融合存储与计算的新型神经网络推理架构，减少数据搬运开销。
• 采用高效的片上网络和内存层次结构，优化了能效比和推理速度。

🏷 关键词

📄 原文摘要

Rathinakumar Appuswamy, Pallab Datta, Michael V. Debole, Steven K. Esser, Carlos Ortega Otero, Jun Sawada, Brian Taba, Arnon Amir, Deepika Bablani, Peter J. Carlson, Myron D. Flickner, Rajamohan Gandhasri, Guillaume J. Garreau, Megumi Ito, Jennifer L. Klamo, Jeffrey A. Kusnitz, Nathaniel J. McClatchey, Jeffrey L. McKinstry, Yutaka Nakamura, Tapan K. Nayak, William P. Risk, Kai Schleupen, Ben Shaw, Jay Sivagnaname, Daniel F. Smith, Ignacio Terrizzano, Takanori Ueda, Dharmendra Modha IBM Research The Deep Neural Network (DNN) era was ushered in by the triad of algorithms, big data, and more powerful hardware processors for training large-scale neural networks. Now, the ubiquitous deployment of DNNs for neural inference in edge, embedded, and data center applications demands more power-efficient hardware processors, while attaining increasingly higher computational performance. To address this Inference Challenge,

👥 作者与机构

Andrew S. Cassidy, John V. Arthur, Filipp Akopyan, Alexander Andreopoulos,