Transfer learning on edge is challenging due to on-device limited resources. Existing work addresses this issue by training a subset of parameters or adding model patches. Developed with inference in mind, Inverted Residual Blocks (IRBs) split a convolutional layer into depthwise and pointwise convolutions, leading to more stacking layers, e.g., convolution, normalization, and activation layers. Though they are efficient for inference, IRBs require that additional activation maps are stored in memory for training weights for convolution layers and scales for normalization layers. As a result, their high memory cost prohibits training IRBs on resource-limited edge devices, and making them unsuitable in the context of transfer learning. To address this issue, we present MobileTL, a memory and computationally efficient on-device transfer learning method for models built with IRBs. MobileTL trains the shifts for internal normalization layers to avoid storing activation maps for the backward pass. Also, MobileTL approximates the backward computation of the activation layer (e.g., Hard-Swish and ReLU6) as a signed function which enables storing a binary mask instead of activation maps for the backward pass. MobileTL fine-tunes a few top blocks (close to output) rather than propagating the gradient through the whole network to reduce the computation cost. Our method reduces memory usage by 46% and 53% for MobileNetV2 and V3 IRBs, respectively. For MobileNetV3, we observe a 36% reduction in floating-point operations (FLOPs) when fine-tuning 5 blocks, while only incurring a 0.6% accuracy reduction on CIFAR10. Extensive experiments on multiple datasets demonstrate that our method is Pareto-optimal (best accuracy under given hardware constraints) compared to prior work in transfer learning for edge devices.
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Local Binary Pattern Networks
Emerging edge devices such as sensor nodes are increasingly being tasked with non-trivial tasks related to sensor data processing and even application-level inferences from this sensor data. These devices are, however, extraordinarily resource-constrained in terms of CPU power (often Cortex M0-3 class CPUs), available memory (in few KB to MBytes), and energy. Under these constraints, we explore a novel approach to character recognition using local binary pattern networks, or LBPNet, that can learn and perform bit-wise operations in an end-to-end fashion. LBPNet has its advantage for characters whose features are composed of structured strokes and distinctive outlines. LBPNet uses local binary comparisons and random projections in place of conventional convolution (or approximation of convolution) operations, providing an important means to improve memory efficiency as well as inference speed. We evaluate LBPNet on a number of character recognition benchmark datasets as well as several object classification datasets and demonstrate its effectiveness and efficiency.
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- NSF-PAR ID:
- 10166838
- Date Published:
- Journal Name:
- IEEE Winter Conference on Applications of Computer Vision
- ISSN:
- 2472-6796
- Page Range / eLocation ID:
- 825-834
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WACV45572.2020.9093550
