Hyperdimensional (HD) computing is attractive for low-power platforms, but common encoders flatten inputs and treat neighboring features as independent, discarding spatial structure and inflating the cost of a dense F×D apply. We present MESHHD, a spatially aware, relative and multi-scale base that maps 2D coordinates with random Fourier features to approximate a distance kernel; nearby locations receive similar hypervectors regardless of absolute position. We further introduce a compact Kronecker-structured apply that realizes the bundled base with three small GEMMs, reducing arithmetic and weight movement from O(FD) toward a near-linear form while preserving encoder semantics. Our experimental results show that MESHHD consistently improves accuracy over the state-of-the-art nonlinear HD encoders, especially at smaller D, and reduces per-batch encoding time by ~ 3×, with up to 10× savings in encoder MACs/state at D=10,000.

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.

We have seen many successful deployments of deep learning accelerator designs on different platforms and technologies, e.g., FPGA, ASIC, and Processing In-Memory platforms. However, the size of the deep learning models keeps increasing, making computations a burden on the accelerators. A naive approach to resolve this issue is to design larger accelerators; however, it is not scalable due to high resource requirements, e.g., power consumption and off-chip memory sizes. A promising solution is to utilize multiple accelerators and use them as needed, similar to conventional multiprocessing. For example, for smaller networks, we may use a single accelerator, while we may use multiple accelerators with proper network partitioning for larger networks. However, partitioning DNN models into multiple parts leads to large communication overheads due to inter-layer communications. In this paper, we propose a scalable solution to accelerate DNN models on multiple devices by devising a new model partitioning technique. Our technique transforms a DNN model into layer-wise partitioned models using an autoencoder. Since the autoencoder encodes a tensor output into a smaller dimension, we can split the neural network model into multiple pieces while significantly reducing the communication overhead to pipeline them. Our evaluation results conducted on state-of-the-art deep learning models show that the proposed technique significantly improves performance and energy efficiency. Our solution increases performance and energy efficiency by up to 30.5% and 28.4% with minimal accuracy loss as compared to running the same model on pipelined multi-block accelerators without the autoencoder.