Add to ORKGEdge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Google products such as Coral and Pixel devices. In this paper, we first discuss the major microarchitectural details of Edge TPUs. Then, we extensively evaluate three classes of Edge TPUs, covering different computing ecosystems, that are either currently deployed in Google products or are the product pipeline, across 423K unique convolutional neural networks. Building upon this extensive study, we discuss critical and interpretable microarchitectural insights about the studied classes of Edge TPUs. Mainly, we discuss how Edge TPU accelerators perform across convolutional neural networks with different structures. Finally, we present our ongoi...
Plenty of edge deep learning accelerators are proposed to speed up the inference of deep learning al...
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series...
Computing has undergone a significant transformation over the past two decades, shifting from a mach...
Tensor Processing Units (TPUs) are specialized hardware accelerators for deep learning developed by ...
This paper explores the performance of Google’s Edge TPU on feed-forward neural networks. We conside...
The design of a Convolutional Neural Network suitable for efficient execution on embedded edge-proce...
Computer science and engineering have evolved rapidly over the last decade offering innovative Machi...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Implementing embedded neural network processing at the edge requires efficient hardware acceleration...
Internet of Things (IoT) edge devices have small amounts of memory and limited computational power. ...
The popularity of Deep Learning (DL) has grown exponentially in all scientific fields, included part...
During the last years, Convolutional Neural Networks have been used for different applications thank...
In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has b...
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy cons...
Most real-time computer vision applications, such as pedestrian detection, augmented reality, and vi...
Plenty of edge deep learning accelerators are proposed to speed up the inference of deep learning al...
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series...
Computing has undergone a significant transformation over the past two decades, shifting from a mach...
Tensor Processing Units (TPUs) are specialized hardware accelerators for deep learning developed by ...
This paper explores the performance of Google’s Edge TPU on feed-forward neural networks. We conside...
The design of a Convolutional Neural Network suitable for efficient execution on embedded edge-proce...
Computer science and engineering have evolved rapidly over the last decade offering innovative Machi...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Implementing embedded neural network processing at the edge requires efficient hardware acceleration...
Internet of Things (IoT) edge devices have small amounts of memory and limited computational power. ...
The popularity of Deep Learning (DL) has grown exponentially in all scientific fields, included part...
During the last years, Convolutional Neural Networks have been used for different applications thank...
In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has b...
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy cons...
Most real-time computer vision applications, such as pedestrian detection, augmented reality, and vi...
Plenty of edge deep learning accelerators are proposed to speed up the inference of deep learning al...
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning models for Time Series...
Computing has undergone a significant transformation over the past two decades, shifting from a mach...