DeepER-HD: An Error Resilient HyperDimensional Computing Framework with DNN Front-End for Feature Selection

Brain-inspired hyperdimensional (HD) computing models mimic cognition through combinatorial bindings of biological neuronal data represented by high-dimensional vectors and related operations. However, the efficacy of HD computing depends strongly on input signal and data features used to realize such bindings. In this paper, we propose a new HD-computing framework based on a co-trainable DNN-based feature extractor pre-processor and a hyperdimensional computing system. When trained with restrictions on the ranges of hypervector elements for resilience to memory access errors, the framework achieves up to 135% accuracy improvement over baseline HD-computing for error-free operation and up to three orders of magnitude improvement in error resilience compared to the state-of-the-art. Results for a range of applications from image classification, face recognition, human activity recognition and medical diagnosis are presented and demonstrate the viability of the proposed ideas.