EDBT 2026 Demo / reviewers in the wild / expert
Andrew Ling
dblp:194/3960
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Hardware accelerators and domain-specific architectures · 79% Reconfigurable computing and FPGAs · 21% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware accelerators and domain-specific architectures › machine learning accelerator › neural network accelerator
FPGA-based neural network accelerator |
0.7 | 2 | 2020 | FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10 · FPGA 2020 The Role of FPGAs in Deep Learning · FPGA 2017 |
Hardware accelerators and domain-specific architectures
machine learning accelerator |
0.7 | 2 | 2020 | FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10 · FPGA 2020 The Role of FPGAs in Deep Learning · FPGA 2017 |
Hardware accelerators and domain-specific architectures › machine learning accelerator
CNN accelerator |
0.4 | 1 | 2020 | FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10 · FPGA 2020 |
Hardware accelerators and domain-specific architectures
deep learning |
0.1 | 1 | 2017 | The Role of FPGAs in Deep Learning · FPGA 2017 |
Reconfigurable computing and FPGAs
FPGA accelerator |
0.1 | 1 | 2017 | The Role of FPGAs in Deep Learning · FPGA 2017 |
Methods — techniques the papers use, named apart from their topics
caffe · 0.4OpenCL · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10abstractDeep learning has becoming increasingly more popular in recent years, and there are many popular frameworks in the market accordingly, such as Caffe, TensorFlow and Pytorch. All these frameworks natively support CPUs and GPGPUs. However, FPGAs still cannot provide a comprehensive support by these frameworks for deep learning development, especially for the training phase. In this paper, we firstly propose the FeCaffe, i.e. FPGA-enabled Caffe, a hierarchical software and hardware design methodology based on the Caffe, to enable FPGA to support CNN training features. Moreover, we provide some benchmarks of popular CNN networks with FeCaffe, and further analysis in details accordingly. Finally, some optimization directions including FPGA kernel design, system pipeline, network architecture, user case application and heterogeneous platform levels, have been proposed gradually. The result demonstrates the proposed FeCaffe can support almost full features for training and inference respectively with high degree of design flexibility, expansibility and reusability for deep learning development. Compared to prior studies, our architecture can support more network and training settings and current configuration can achieve 6.4x and 8.4x average execution time improvement for forward and backward respectively for LeNet. Andrew Ling, Dian Gu |
FPGA | 4 |
| 2017 | The Role of FPGAs in Deep Learning
Andrew Ling |
FPGA | 1 |