Name
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) are widely used for image tasks in both embedded systems and data centers. Particularly, when deploying CNNs in a data center, achieving high accuracy and low latency are important for various tasks such as the image processing of streaming videos. However, conventional CNN accelerators often use architectures with a deep pipeline, resulting in not only high throughput but also high latency. We propose an FPGA-based inference accelerator for randomly wired neural networks (RWNNs), whose layer structures are based on random graph models. Because RWNN can be processed in parallel, we can reduce the latency by concurrently using multiple computational units. We use the HBM2 to store feature maps, as multiple computing units need to simultaneously access different feature maps. In addition, the HBM channels and computational units are connected using a crossbar switch to efficiently transfer the feature maps. We allocate each layer to computational units using a simple heuristic algorithm. In addition, we allocate each layer to the HBM channels by coloring a conflict graph built based on the allocated schedule. This makes it possible for the computational units to access HBM channels in parallel. We implemented our architecture on an Alveo U50 FPGA and compared it with a FPGA-based inference accelerator that targets ResNet-50. In the ImageNet image classification task, we could process an image in 16.6 ms, which is 43% lower than that for a conventional accelerator. In addition, our accelerator could reduce the latency by 5.53 times compared with a CPU and 1.98 times compared with a GPU. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/FPL50879.2020.00056 | 2020 30th International Conference on Field-Programmable Logic and Applications (FPL) |
Keywords | DocType | ISBN |
Convolutional Neural Network, FPGA, hardware accelerator, randomly wired neural networks | Conference | 978-1-7281-9902-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryosuke Kuramochi | 1 | 0 | 2.70 |
| Hiroki Nakahara | 2 | 155 | 37.34 |