第六章 EIE-用于稀疏神经网络的高效推理引擎 目测和发在ISCA2016的论文EIE: Efficient Inference Engine on Compressed Deep Neural Network内容一致,补了一些图。是一个神经网络加速器,用硬件加速训练好的网络模型。 6.1 Introduction 为了评估EIE性能,为其建立了行为级描述和RTL级模型,并对RTL模型进行了综合和布局布线...
Hardware for Deep Learning Accelerationdoi:10.1002/aisy.202300762Song, ChoongseokYe, ChangMinSim, YongukJeong, Doo SeokAdvanced Intelligent Systems (2640-4567)
As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are
“deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During...
3. Hardware for Efficient Inference 这个方向各种硬件的共同目的是减少内存的读取(minimize memory access)。硬件需要能用压缩过的神经网络做预测。 EIE(Efficient Inference Engine)(Han et al. ISCA 2016):稀疏权重(扔掉为0的权重)、稀疏激活值(扔掉为0的激活值)、Weight Sharing(4-bit)。
This forms a truly end-to-end, from software-to-hardware open source stack for deep learning systems. 展开 关键词: Computer Science - Machine Learning Computer Science - Distributed Parallel and Cluster Computing Statistics - Machine Learning ...
A hard drive can be a significant bottleneck in some cases for deep learning. If your data set is large you will typically have some of it on your SSD/hard drive, some of it in your RAM, and two mini-batches in your GPU RAM. To feed the GPU constantly, we need to provide new mi...
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models 术语 摘要 深度学习推荐模型(DLRM)已经在 Meta 的许多关键商业服务中使用, 并且在其数据中心基础设施需求方面是最大的单一 AI 应用程序。论文提出了Neo:一种针对大规模 DLRM 训练场景, 采用软硬件协同方案设计的高...
Deep Learning on FPGAs: Past, Present, and Future The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Inste... G Lacey,GW Taylor,S Areibi 被引量: 63发表: 2016年 Urology training: past, ...
Hardware for machine learning: Challenges and opportunities[C]. Custom Integrated Circuits Conference (CICC). IEEE, 2018: 1-8. Chen Y H, Emer J, Sze V. Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks[C]. ACM SIGARCH Computer Architecture News. ...