Abstract
High computational requirement and rigorous memory cost are the significant issues which limit Convolutional Neural Networks' deployability in resource-constrained environments typically found in edge devices of Internet-of-Things (IoT). To address the problem, binary and ternary networks have been proposed to constrain the weights to reduce computational and memory costs. However, owing to the binary or ternary values, the backward propagations are not as efficient as normal during training, which makes it tough to train in edge devices. In this paper, we find a different way to resolve the problem and propose Fixed-Sign Binary Neural Network (FSB), which decomposes convolution kernel into sign and scaling factor as the prior researches but only trains the scaling factors instead of both. By doing so, our FSB avoids the sign involved in backward propagations and makes models easy to be deployed and trained in the IoT devices. Meanwhile, the convolution-acceleration architecture which we design for our FSB results in a reduced computing burden while achieving the same performance. Thanks to the efficiency of our FSB, even though we randomly initialize the sign and fix it to be untrainable, our FSB still has remarkable performances.
Highlights
Convolutional Neural Networks (CNNs) have been rapidly developing and achieved remarkable improvements in most Artificial Intelligence (AI) domains, most of Convolutional neural network (CNN) cannot be directly deployed on the resource-constrained devices
Just as much research focuses on how to finetune the weights with only two or three possible values to achieve a good tradeoff between accuracy and complexity, we are motivated to raise the questions: What role does the sign value of the convolutions play in the convolutional neural networks? Or, if we fix the sign values and only train the scaling factors, can we obtain a CNN with a satisfactory accuracy in the validation set?
As we only bring the scaling factors into the forward and the backward propagations, we name our algorithm as Fixed-Sign Binary Neural Network (FSB) in this paper, which shrinks the size of the training parameter and brings efficiency to model update and deployment
Summary
Convolutional Neural Networks (CNNs) have been rapidly developing and achieved remarkable improvements in most Artificial Intelligence (AI) domains, most of CNNs cannot be directly deployed on the resource-constrained devices. We can quantize the weights with ternary values [12], [13], [19]–[21] These algorithms can achieve over 10× model compression rate and accelerate convolutional operations as well. Due to the promising tradeoff between performance and efficiency, we can deploy the CNNs with binary weights on the edge devices. There are many frameworks proposed for CNNs inference on edge devices, but most of these frameworks are software based, which means that we cannot accelerate the computation through the hardware layer To address these problems, we propose Fixed-Sign Binary Neural Network. We transfer most of the convolutions calculation from software to hardware and reduce the size of parameters for model update, which results in more energy saving and calculation efficiency. We present experimental results over widely-used datasets to show the effectiveness of our algorithm; and conclude with a summary and future work
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