Brain-inspired Hyperdimensional (HD) computing is a new computing paradigm emulating the neuron's activity in high-dimensional space. The first step in HD computing is to map each data point into high-dimensional space (e.g., 10,000), which requires the computation of thousands of operations for each element of data in the original domain. Encoding alone takes about 80% of the execution time of training. In this paper, we propose BRIC, a fully binary Brain-Inspired Classifier based on HD computing for energy-efficient and high-accuracy classification. BRIC introduces a novel encoding module based on random projection with a predictable memory access pattern which can efficiently be implemented in hardware. BRIC is the first HD-based approach which provides data projection with a 1:1 ratio to the original data and enables all training/inference computation to be performed using binary hypervectors. To further improve BRIC efficiency, we develop an online dimension reduction approach which removes insignificant hypervector dimensions during training. Additionally, we designed a fully pipelined FPGA implementation which accelerates BRIC in both training and inference phases. Our evaluation of BRIC a wide range of classification applications show that BRIC can achieve 64.1× and 9.8× (43.8× and 6.1×) energy efficiency and speed up as compared to baseline HD computing during training (inference) while providing the same classification accuracy.

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.