MMH Shuvo, SK Islam, J Cheng… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data …
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage …
CJ Wu, D Brooks, K Chen, D Chen… - … symposium on high …, 2019 - ieeexplore.ieee.org
At Facebook, machine learning provides a wide range of capabilities that drive many aspects of user experience including ranking posts, content understanding, object detection …
Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous compute intensity. Fully realizing the potential of acceleration in this domain requires …
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …
Recent advances in state-of-the-art DNN architecture design have been moving toward Transformer models. These models achieve superior accuracy across a wide range of …
Processing In-Memory (PIM) has shown a great potential to accelerate inference tasks of Convolutional Neural Network (CNN). However, existing PIM architectures do not support …
YN Wu, JS Emer, V Sze - 2019 IEEE/ACM International …, 2019 - ieeexplore.ieee.org
With Moore's law slowing down and Dennard scaling ended, energy-efficient domain- specific accelerators, such as deep neural network (DNN) processors for machine learning …
Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse …