Siamese Networks for Few-Shot Learning on Edge Embedded Devices

Edge artificial intelligence hardware targets mainly inference networks that have been pretrained on massive datasets. The field of few-shot learning looks for methods that allow a network to produce high accuracy even when only a few samples of each class are available. Siamese networks can be used...

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Bibliographic Details
Published in:IEEE journal on emerging and selected topics in circuits and systems 2020-12, Vol.10 (4), p.488-497
Main Authors: Lungu, Iulia Alexandra, Aimar, Alessandro, Hu, Yuhuang, Delbruck, Tobi, Liu, Shih-Chii
Format: Article
Language:English
Subjects:
Accelerators
Accuracy
Artificial intelligence
Artificial neural networks
Benchmark testing
Benchmarks
Computer vision
Datasets
deep networks
Edge computing
edge intelligence
Electronic devices
Embedding
few-shot learning
Frames per second
Hardware
Hardware acceleration
hardware accelerators
Inference
Learning
Massive data points
Network architecture
One-shot learning
Platforms
Prototypes
Retraining
Siamese networks
Sparsity
Training
Weight
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Summary:Edge artificial intelligence hardware targets mainly inference networks that have been pretrained on massive datasets. The field of few-shot learning looks for methods that allow a network to produce high accuracy even when only a few samples of each class are available. Siamese networks can be used to tackle few-shot learning problems and are unique because they do not require retraining on the new samples of the new classes. Therefore they are suitable for edge hardware accelerators which often do not include on-chip training capabilities. This work describes improvements to a baseline Siamese network and benchmarking of the improved network on edge platforms. The modifications to the baseline network included adding multi-resolution kernels, a hybrid training process as well a different embedding similarity computation method. This network shows an average accuracy improvement of up to 22% across 4 datasets in a 5-way, 1-shot classification task. Benchmarking results using three edge computing platforms (NVIDIA Jetson Nano, Coral Edge TPU and a custom convolutional neural network accelerator) show that a Siamese classifier can run on these devices at reasonable frame rates for real-time performance, between 3 frames per second (FPS) on Jetson Nano and 60 FPS on the Edge TPU. By increasing the weight sparsity during training, the inference time of a network with 25% weight sparsity increases by 10 FPS but with only 1% drop in accuracy.
ISSN:2156-3357
2156-3365
DOI:10.1109/JETCAS.2020.3033155