Today's applications are using machine learning algorithms to analyze the data collected from a swarm of devices on the Internet of Things (IoT). However, most existing learning algorithms are overcomplex to enable real-time learning on IoT devices with limited resources and computing power. Recently, Hyperdimensional computing (HDC) is introduced as an alternative computing paradigm for enabling efficient and robust learning. HDC emulates the cognitive task by representing the values as patterns of neural activity in high-dimensional space. HDC first encodes all data points to high-dimensional vectors. It then efficiently performs the learning task using a well-defined set of operations. Existing HDC solutions have two main issues that hinder their deployments on low-power embedded devices: (i) the encoding module is costly, dominating 80% of the entire training performance, (ii) the HDC model size and the computation cost grow significantly with the number of classes in online inference.In this paper, we proposed a novel architecture, LookHD, which enables real-time HDC learning on low-power edge devices. LookHD exploits computation reuse to memorize the encoding module and simplify its computation with single memory access. LookHD also address the inference scalability by exploiting HDC governing mathematics that compresses the HDC trained model into a single hypervector. We present how the proposed architecture can be implemented on the existing low power architectures: ARM processor and FPGA design. We evaluate the efficiency of the proposed approach on a wide range of practical classification problems such as activity recognition, face recognition, and speech recognition. Our evaluations show that LookHD can achieve, on average, $ 28.3\times$ faster and $ 97.4\times$ more energy-efficient training as compared to the state-of-the-art HDC implemented on the FPGA. Similarly, in the inference, LookHD is $ 2.2\times$ faster, $ 4.1\times$ more energy-efficient, and has $ 6.3\times$ smaller model size than the same state-of-the-art algorithms.

Brain-inspired hyperdimensional (HD) computing models cognition by exploiting properties of high dimensional statistics-high-dimensional vectors, instead of working with numeric values used in contemporary processors. A fundamental weakness of existing HD computing algorithms is that they require to use floating point models in order to provide acceptable accuracy on realistic classification problems. However, working with floating point values significantly increases the HD computation cost. To address this issue, we proposed QuantHD, a novel framework for quantization of HD computing model during training. QuantHD enables HD computing to work with a low-cost quantized model (binary or ternary model) while providing a similar accuracy as the floating point model. We accordingly propose an FPGA implementation which accelerates HD computing in both training and inference phases. We evaluate QuantHD accuracy and efficiency on various real-world applications, and observe that QuantHD can achieve on average 17.2% accuracy improvement as compared to the existing binarized HD computing algorithms which provide a similar computation cost. In terms of efficiency, QuantHD FPGA implementation can achieve on average 42.3× and 4.7× (34.1× and 4.1×) energy efficiency improvement and speedup during inference (training) as compared to the state-of-the-art HD computing algorithms.

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 7.11× faster and 12.6× energy efficiency as compared to the CPU implementation.

Hyperdimensional (HD) computing is a mathematical framework, inspired by neuroscience, which can be used to represent many machine learning (ML) problems. Data is first encoded into high dimensional space (on the order of 103or 104dimensions) to create hypervectors. HD computing combines these hypervectors to create a model used for inference. However, due to the high dimensionality of the hypervectors, inference in HD is very expensive, especially when it runs on embedded devices with limited resources. One naive approach to improve the efficiency of HD computing is to simply lower the dimensionality of hypervectors, which comes with a corresponding loss in accuracy. However, if the data is compressed intelligently, we can reduce the dimensionality of an HD model without sacrificing accuracy. To that end, we propose CompHD, a novel approach for compressing HD models while maintaining the accuracy of the original model. CompHD utilizes the mathematics of high-dimensional spaces to compress hypervectors into shorter vectors while maintaining the information of full length hypervectors. We evaluated the efficiency of CompHD on a variety of applications. Our results show that CompHD can reduce model size by an average of 69.7%, resulting in a execution time speed up of 4.1 × and improving energy efficiency by 74% while maintaining the accuracy of the original model. This enables more low powered IoT devices to utilize HD computing for ML problems.