In this paper, we propose BioHD, a novel genomic sequence searching platform based on Hyper-Dimensional Computing (HDC) for hardware-friendly computation. BioHD transforms inherent sequential processes of genome matching to highly-parallelizable computation tasks. We exploit HDC memorization to encode and represent the genome sequences using high-dimensional vectors. Then, it combines the genome sequences to generate an HDC reference library. During the sequence searching, BioHD performs exact or approximate similarity check of an encoded query with the HDC reference library. Our framework simplifies the required sequence matching operations while introducing a statistical model to control the alignment quality. To get actual advantage from BioHD inherent robustness and parallelism, we design a processing in-memory (PIM) architecture with massive parallelism and compatible with the existing crossbar memory. Our PIM architecture supports all essential BioHD operations natively in memory with minimal modification on the array. We evaluate BioHD accuracy and efficiency on a wide range of genomics data, including COVID-19 databases. Our results indicate that PIM provides 102.8× and 116.1× (9.3× and 13.2×) speedup and energy efficiency compared to the state-of-theart pattern matching algorithm running on GeForce RTX 3060 Ti GPU (state-of-the-art PIM accelerator).
Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and nontransparent notion of memorization. Brain-inspired HyperDimensional Computing (HDC) is introduced as a model of human memory. Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition. In this manuscript, we introduce a brain-inspired system that represents HDC memorization capability over a graph of relations. We propose GrapHD, hyperdimensional memorization that represents graph-based information in high-dimensional space. GrapHD defines an encoding method representing complex graph structure while supporting both weighted and unweighted graphs. Our encoder spreads the information of all nodes and edges across into a full holistic representation so that no component is more responsible for storing any piece of information than another. Then, GrapHD defines several important cognitive functionalities over the encoded memory graph. These operations include memory reconstruction, information retrieval, graph matching, and shortest path. Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing and reasoning over memorization graph, and (3) enables holographic brain-like computation with substantial robustness to noise and failure.
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