BRIC: Locality-based Encoding for Energy-Efficient Brain-Inspired Hyperdimensional Computing

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 64.1x and 9.8x (43.8x and 6.1x) energy efficiency and speed up as compared to baseline HD computing during training (inference) while providing the same classification accuracy.