UNPU: A 50.6TOPS/W Unified Deep Neural Network Accelerator with 1b-to-16b Fully-Variable Weight Bit-Precision

📋 论文概要

提出了一种名为UNPU的统一深度神经网络加速器，支持1b到16b完全可变的权重位精度，能够高效加速卷积层、全连接层和循环层，解决了现有加速器仅支持部分层类型的问题，实现了50.6TOPS/W的高能效。

💡 主要创新点

• 提出统一架构，同时支持卷积层、全连接层和循环层，无需分离处理单元。
• 实现1b到16b完全可变的权重位精度，灵活适应不同精度需求。
• 通过优化数据流和计算单元，达到50.6TOPS/W的顶尖能效比。

🏷 关键词

📄 原文摘要

deep learning algorithms from face recognition to emotion recognition in mobile or embedded environments [3]. However, most works accelerate only the convolutional layers (CLs) or fully-connected layers (FCLs), and different DNNs, such as those containing recurrent layers (RLs) (useful for emotion recognition) have not been supported in hardware. A combined CNN-RNN accelerator [1], separately optimizing the computation-dominant CLs, and memory-dominant RLs or FCLs, was reported to increase overall performance, however, the number of processing elements (PEs) for CLs and RLs was limited by their area and consequently, performance was suboptimal in scenarios requiring only CLs or only RLs. Although the PEs for RLs can be reconfigured into PEs for CLs or vice versa, only a partial reconfiguration was possible resulting in marginal

👥 作者与机构

Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin,

Sangyeob Kim, Hoi-Jun Yoo KAIST, Daejeon, Korea Deep neural network (DNN) accelerators [1-3] have been proposed to accelerate