Publication
In the Internet of Things (IoT), the large volume of data generated by sensors poses significant computational challenges in resource-constrained environments. Most existing machine learning algorithms are unable to train a proper model using a significantly small amount of labeled data available in practice. In this paper, we propose SemiHD, a novel semi-supervised algorithm based on brain-inspired HyperDimensional (HD) computing. SemiHD performs the cognitive task by emulating neuron's activity in high-dimensional space. SemiHD maps data points into high-dimensional space and trains a model based on the available labeled data. To improve the quality of the model, SemiHD iteratively expands the training data by labeling data points which can be classified by the current model with high confidence. We also proposed a framework which enables users to trade accuracy for efficiency and select the desired reliability of the model in detecting out of scope data. We have evaluated SemiHD's accuracy and efficiency on a wide range of classification applications and two types of embedded devices: Raspberry Pi 3 and Kintex-7 FPGA. Our evaluation shows that SemiHD can improve the classification accuracy of supervised HD by 10.2% on average (up to 27.3%). In addition, we observe that SemiHD FPGA implementation achieves 7.11x faster and 12.6x energy efficiency as compared to the CPU implementation.