Scalable video retrieval is increasingly challenging as datasets reach tens of millions of videos. Current text-to-video retrieval (T2VR) methods either compress videos into single dense vectors, losing segment-level detail, or expand them into multi-frame representations, incurring prohibitive storage and search costs. We propose a binary hyperdimensional representation that encodes each video into a compact 3,072-dimension hypervector, preserving semantic fidelity while reducing memory via bit-packing. To leverage the properties of hypervectors for sublinear search, we introduce Hypervector Retrieval (HVR), a frequency-aware inverted index that prioritizes rare informative positions and refines candidates using GPU-accelerated Hamming search. Experiments show that our approach matches or exceeds dense baselines for T2VR and surpasses state-of-the-art partially relevant video retrieval (PRVR) by over 5% Recall@ 10 on ActivityNet. At scale, HVR processes over 2,000 queries per second on 10M videos, maintains recall within 1% of exact search, and achieves 5.3× greater storage capacity than CLIP4Clip and over 2,116× over MS-SL.

Retrieval-Augmented Generation (RAG) systems typically rely on dense floating-point embeddings to retrieve relevant documents, but this approach incurs significant memory and compute costs at scale. We propose a Hyperdimensional Computing (HDC) framework that projects transformer token embeddings into high-dimensional binary hypervectors, which are aggregated into compact document representations. To support sublinear search, we introduce HD-NSW, a graph-based index inspired by navigable small-world networks. HD-NSW clusters similar hypervectors into bundled centroids and connects them with sparse Hammingdistance edges, enabling efficient, beam-guided traversal entirely in the binary domain. Across 15 BEIR benchmarks and synthetic Gaussian mixture corpora, HD-NSW achieves over 99% of dense retrieval quality, reduces memory usage by $8 \times$, and supports over 860 queries per second at 10 million documents while maintaining over 80% throughput at 40 million documents. At five million documents, HD-NSW achieves $7.68 \times$ higher throughput compared to state of the art approximate nearest neighbor methods. Beyond this point, competing baselines encounter memory exhaustion, while HD-NSW continues scaling and maintains high throughput at larger corpus sizes.

We propose an advanced hyperdimensional computing (HDC) framework for regression tasks, addressing the limitations of existing methods through three key innovations: fine-grained feature encoding, confidence-based inference, and dimension-split boosting for scalable training. By preserving inter-feature relationships and enabling efficient computation on high-dimensional spaces, the framework achieves superior accuracy and efficiency across diverse benchmarks. Our evaluation demon-strates that HB R F achieves significant improvements in prediction quality and computational efficiency as compared to the state-of-the-art HDC- based regression by 31% and 54.8 %, respectively.

Traditional federated learning frameworks, often reliant on deep neural networks, face challenges related to computational demands and privacy risks. In this paper, we present a novel Hyperdimensional (HD) Computing-based federated learning framework designed for resource-constrained mobile robots. Unlike other HD-based learning, our approach introduces dynamic encoding, which improves both model accuracy and privacy by continuously updating hypervector representations. To further address the issue of imbalanced data, especially prevalent in robotics tasks, we propose a hypervector oversampling technique, enhancing model robustness. Extensive evaluations on LiDAR-equipped mobile robots demonstrate that our oversampling method outperforms state-of-the-art HD computing frameworks, achieving up to a 22.9% increase in accuracy while maintaining computational efficiency.

Hyperdimensional (HD) Computing is a lightweight representation system that symbolizes data as high-dimensioned vectors. HD computing has been growing in popularity in recent years as an alternative to deep neural networks mainly due to its simple and efficient operations. In HD-based learning frameworks, the encoding of the high dimensional representations are widely cited to be the most contributing procedure to accuracy and efficiency. However, throughout HD computing's history, the encoder has largely remained static. In this work, we explore methods for a dynamic encoder that yields better representations as training progresses. Our proposed method, SEP, achieves accuracies comparable to state-of-the-art HD-based methods proposed in the literature; more notably, our solutions outperform existing work at lower dimensions while maintaining a relatively small dimension of$D=3,000$, which equates to an average of$3.32\times$faster inference.

Hyperdimensional (HD) computing is an emerging paradigm inspired by human cognition, utilizing high-dimensional vectors to represent and learn information in a lightweight manner based on its simple and efficient operations. In HD-based learning frameworks, the encoding of the high dimensional representations is the most contributing procedure to accuracy and efficiency. However, throughout HD computing's history, the encoder has largely remained static, which leads to sub-optimal hypervector representations and excessive dimensionality requirements. In this paper, we propose novel forward-only training methods for HD encoders, Stochastic Error Projection (SEP) and Input Modulated Projection (IMP), which dynamically adjust the encoding process during training. Our methods achieve accuracies comparable to state-of-the-art HD-based techniques, with SEP and IMP outperforming existing methods by 5.49% on average at a reduced dimensionality of D = 3,000. This reduction in dimensionality results in a 3.32x faster inference.

Efficiency and performance are significant challenges in applying Machine Learning (ML) to robotics, especially in energy-constrained real-world scenarios. In this context, Hyperdimensional Computing offers an energy-efficient alternative but has been underexplored in robotics. We introduce ReactHD, an HDC-based framework tailored for perception-action-based learning for sensorimotor controls of robot tasks. ReactHD employs hypervectors to encode sensory inputs and learn the suitable high-dimensional pattern for robot actions. It also integrates two HD-based lightweight symbolic learning techniques: HDC-based supervised learning by demonstration (HDC-IL) and HD-Reinforcement Learning (HDC-RL) to enable precise, reactive robot behaviors in complex environments. Our empirical evaluations show that ReactHD achieves robust and accurate learning outcomes comparable to state-of-the-art deep learning while substantially improving the performance and energy consumption efficiency by 14.2× and 15.3×. To the best of our knowledge, ReactHD is the first HDC-based framework deployed in real-world settings.

Hyperdimensional computing (HDC) is a computing paradigm inspired by the mechanisms of human memory, characterizing data through high-dimensional vector representations, known as hypervectors. Recent advancements in HDC have explored its potential as a learning model, leveraging its straightforward arithmetic and high efficiency. The traditional HDC frameworks are hampered by two primary static elements: randomly generated encoders and fixed learning rates. These static components significantly limit model adaptability and accuracy. The static, randomly generated encoders, while ensuring high-dimensional representation, fail to adapt to evolving data relationships, thereby constraining the model’s ability to accurately capture and learn from complex patterns. Similarly, the fixed nature of the learning rate does not account for the varying needs of the training process over time, hindering efficient convergence and optimal performance. This article introducesTrainableHD, a novel HDC framework that enables dynamic training of the randomly generated encoder depending on the feedback of the learning data, thereby addressing the static nature of conventional HDC encoders.TrainableHDalso enhances the training performance by incorporating adaptive optimizer algorithms in learning the hypervectors. We further refineTrainableHDwith effective quantization to enhance efficiency, allowing the execution of the inference phase in low-precision accelerators. Our evaluations demonstrate thatTrainableHDsignificantly improves HDC accuracy by up to 27.99% (averaging 7.02%) without additional computational costs during inference, achieving a performance level comparable to state-of-the-art deep learning models. Furthermore,TrainableHDis optimized for execution speed and energy efficiency. Compared to deep learning on a low-power GPU platform like NVIDIA Jetson Xavier,TrainableHDis 56.4 times faster and 73 times more energy efficient. This efficiency is further augmented through the use of Encoder Interval Training (EIT) and adaptive optimizer algorithms, enhancing the training process without compromising the model’s accuracy.

HD computing is a symbolic representation system which performs various learning tasks in a highly-parallelizable and binary-centric way by drawing inspiration from concepts in human long-term memory. However, the current HD computing is ineffective in extracting high-level feature information for image data. In this paper, we present a neuro-symbolic approach called NSHD, which integrates CNNs and Hyperdimensional (HD) learning techniques to provide efficient learning with state-of-the-art quality. We devise the HD training procedure, which fully integrates knowledge from the deep learning model through a distillation process with optimized computation costs due to the integration. Our experimental results show that NSHD provides high energy efficiency as compared to CNN, e.g., up to 64% with comparable accuracy, and can outperform the learning quality when more computing resources are allowed. We also show the symbolic nature of the NSHD can make the learning humnan-interpretable by exploiting the property of HD computing.

Hyperdimensional computing (HDC) is a computing paradigm that draws inspiration from human memory models. It represents data in the form of high-dimensional vectors. Recently, many works in literature have tried to use HDC as a learning model due to its simple arithmetic and high efficiency. However, learning frameworks in HDC use encoders that are randomly generated and static, resulting in many parameters and low accuracy. In this paper, we propose TrainableHD, a framework for HDC that utilizes a dynamic encoder with effective quantization for higher efficiency. Our model considers errors gained from the HD model and dynamically updates the encoder during training. Our evaluations show that TrainableHD improves the accuracy of the HDC by up to 22.26% (on average 3.62%) without any extra computation costs, achieving a comparable level to state-of-the-art deep learning. Also, the proposed solution is 56.4 x faster and 73 x more energy efficient as compared to the deep learning on NVIDIA Jetson Xavier, a low-power GPU platform.

Face detection is an essential component of many tasks in computer vision with several applications. However, existing deep learning solutions are significantly slow and inefficient to enable face detection on embedded platforms. In this paper, we propose HDFace, a novel framework for highly efficient and robust face detection. HDFace exploits HyperDimensional Computing (HDC) as a neurally-inspired computational paradigm that mimics important brain functionalities towards high-efficiency and noise-tolerant computation. We first develop a novel technique that enables HDC to perform stochastic arithmetic computations over binary hypervectors. Next, we expand these arithmetic for efficient and robust processing of feature extraction algorithms in hyperspace. Finally, we develop an adaptive hyperdimensional classification algorithm for effective and robust face detection. We evaluate the effectiveness of HDFace on large-scale emotion detection and face detection applications. Our results indicate that HDFace provides, on average, 6.1X (4.6X) speedup and 3.0X (12.1X) energy efficiency as compared to neural networks running on CPU (FPGA), respectively.

Brain-inspired hyperdimensional (HD) computing is a new computing paradigm based on theoretical neuroscience to enable efficient learning. In HD computing, the original data are encoded to points in a high-dimensional space to perform learning with lightweight algebra. In this paper, we propose STEMHD that elicits key features from spatiotemporal data along with a hardware design that empowers computation reuse. Our evaluation shows that STEMHD successfully interprets structural data at a low cost achieving higher accuracy than the state-of-the-art methods. Our evaluation shows that STEMHD improves performance and energy efficiency during the model training by 16.3% and 19.7%, respectively, with a negligible accuracy loss of less than 0.25%. For the model inference, we observe the inference speedup of 1.96× on average.