Hyperdimensional Computing (HDC) represents data as high-dimensional hypervectors that are robust and efficient for learning. Existing methods often rely on pseudo-random hypervector generation, which can suffer from poor orthogonality and high variance across runs, ultimately slowing convergence. These approaches typically require numerous iterations (20– 100) to achieve acceptable accuracy. We propose a method that utilizes deterministic Sobol-based linear projections and rank-based retraining to construct more stable and discriminative hypervectors, thereby reducing class confusion. Unlike pseudo-random initialization, our projections guarantee reproducibility and better coverage of the feature space. As a result, our approach achieves up to 97% accuracy in only 5 iterations. This makes our model up to 20× faster while simultaneously improving accuracy.

On-device pose estimation is widely available through models, yet efficiently classifying the resulting landmarks on resource-constrained mobile platforms remains challenging. We propose POSEiDON, a hyperdimensional computing (HDC)-based pose classification pipeline that combines landmark-symbol and joint-angle encodings with low-discrepancy (Sobol) sequences. On a custom 4-class mobile dataset and a 5-class YOGA benchmark, POSEiDON achieves up to 97.79% and 88.44% accuracy, respectively, using a single training pass, while iterative baselines require 10–200 passes or estimators to reach comparable accuracy. An Artix-7 FPGA prototype and an Android implementation further show that the HDC stage adds only marginal power, latency, and memory overhead, indicating that HDC is a promising primitive for lightweight on-device pose recognition.

This paper presents SP-HD, a near-sensor image classification architecture that combines stochastic computing (SC) and hyperdimensional computing (HDC) to enable energy-efficient and compact embedded intelligence. The proposed approach introduces a stochastic projection mechanism that converts input features into bitstreams, enabling bipolar multiplications to be performed with simple logic and in-memory accumulation, thereby eliminating costly multipliers and level hypervectors. A mixed-signal ReRAM-based implementation further reduces data movement by performing projection and accumulation directly within the memory fabric, while binary-weight classification minimizes circuit complexity. SP-HD achieves competitive accuracy across multiple image datasets and delivers 3μJ energy per inference with a compact 2.56mm2hardware footprint, significantly outperforming prior ReRAM compute-in-memory accelerators in both energy and area efficiency.

Hyperdimensional computing (HDC) is a promising approach for energy-efficient edge machine learning (ML), where low latency, low power, and tight memory budgets are essential. However, traditional HDC relies on symbolic binding and pseudo-random high-dimensional vectors, which require large dimensionality and heuristic updates to reach competitive accuracy, limiting deployment on edge hardware. We introduce XL-HD, a deterministic, projection-based, fully learnable HDC framework tailored for in-memory acceleration within edge computing systems. The method uses a fixed Sobol sequence to project binary inputs, extending learning beyond conventional HDC. During training, class prototypes are optimized in real-valued space and later binarized, enabling an entirely binary dot-product inference pipeline ideal for IMC hardware such as ReRAM crossbars. XL-HD achieves competitive accuracy on MNIST, UCIHAR, and ISOLET while maintaining a compact IMC-based inference engine with 0.395 mm2 area and only 0.40 μJ per single-cycle inference.

Hyperdimensional computing (HDC), also known as vector symbolic architecture (VSA), is a brain-inspired paradigm offering lightweight and hardware-efficient cognitive learning. By encoding data into high-dimensional hypervectors (HVs), HDC supports single-pass training and inherent robustness, making it highly attractive for edge AI. Yet, two challenges impede its deployment: efficient on-chip generation of orthogonal HVs and adaptation to dynamic data sizes without costly retraining. This work introduces the Independent and Dynamic VSA (ID-VSA), which advances HDC through five key innovations. First, we propose a compact single-source HV generator based on low-discrepancy (LD) sequences, enabling orthogonal symbol vectors with minimal hardware cost. Second, we presentGaussian Polygon, a multiscale learning mechanism that performs Gaussian-like interpolation directly in the HV domain. Third, we extend HV generation to quasi-normal distributions (QNDs), supporting both symbol and level vectors from the same randomness source. Fourth, we incorporate true-random number generation to exploit device-level noise for unbiased HV creation. Finally, we demonstrate flexible multi-assignment encoding for efficient$n$-gram processing. Evaluations demonstrate the proposed methods achieve accuracy improvements of up to 1.07% and 2.50% for image datasets MNIST and Pneumonia MNIST, and 18.36% on the language dataset over conventional HDC models. For larger-scale workloads, the proposedGaussian Polygon-based designs achieve up to 4.33% improvement on the EuroSAT remote sensing dataset and up to 0.71% improvement on FractureMNIST3D medical dataset. Hardware synthesis in 45 nm technology confirms efficiency, achieving up to$370\times $lower power and$109\times $smaller area, establishingID-VSAas a scalable solution for real-time, hardware-efficient edge AI.

Inspired by the human brain, Hyperdimensional Computing (HDC) processes information efficiently by operating in high-dimensional space using hypervectors. While previous works focus on optimizing pregenerated hypervectors in software, this study introduces a novel on-the-fly vector generation method in hardware with $O(1)$ complexity, compared to the $O(N)$ iterative search used in conventional approaches to find the best orthogonal hypervectors. Our approach leverages Hadamard binary coefficients and unary computing to simplify encoding into addition-only operations after the generation stage in ASIC, implemented using inmemory computing. The proposed design significantly improves accuracy and computational efficiency across multiple benchmark datasets.

Hyperdimensional Computing (HDC) has emerged as a hardware-efficient paradigm for embedded malware detection, offering strong parallelism and low complexity. However, the accuracy and robustness of HDC classifiers remain highly dependent on the diversity and quality of training data, leaving them vulnerable to novel threats. To address this challenge, we introduce a generative adversarial network (GAN)-assisted augmentation framework for the Microprocessor without Interlocked Pipelined Stages-32 (MIPS32) malware generation. The GAN is trained on real-world MIPS32 malware binaries to produce previously unseen instruction sequences. The synthetic code stacks are filtered using a custom MIPS32 assembler for syntactic validation and a Bidirectional Long Short-Term Memory (BiLSTM)-based semantic critic to ensure logical coherence. Only validated samples are retained to expand the training set for the HDC classifier, thereby strengthening generalization and resilience against novel malware variants. Our preliminary results show an average generator loss ($\mathbf{G}$) of 2.68 over 200 epochs and a discriminator loss (D) converging to 0.63, indicating that the GAN is learning to generate realistic and diverse outputs. This hybrid GAN-BiLSTM-HDC framework shows strong potential for enhancing classification accuracy, resilience, and efficiency in resource-constrained, real-time malware detection systems.

Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient alternative solution, making it well-suited for resource-constrained environments, such as wearable devices. Its lightweight architecture and efficient memory management make it ideal for embedded systems, enabling real-time AMS detection with accuracy comparable to traditional ML models. We introduce AMS-HD, a novel framework that leverages custom feature engineering and quasi-random hyper-vector encoding to further enhance the efficiency and accuracy of HDC for AMS detection. The proposed framework demonstrates the potential for seamless integration into wearable biomedical devices for on-the-go health monitoring.

Stochastic computing (SC) leverages random bitstreams to perform arithmetic operations, offering ultra-low-cost, fault-tolerant, and highly parallelizable computations. The quality of these bit-streams is crucial for the accuracy and reliability of SC. This paper introduces TRUE-BSG, a novel true random bit-stream generator designed for fast and energy-efficient SC. Unlike state-of-the-art (SoTA) pseudo-random and quasi-random bit-stream generators, TRUE-BSG utilizes a high-quality true random number generator (TRNG), capable of producing random bits at a rate of 1 Gigabit per second. Our TRNG ensures high entropy and minimal correlation. TRUE-BSG shows comparable accuracy to software-based generators and better energy efficiency than SoTA bit-stream generators, making it an ideal solution for resource-constrained devices.

Hyperdimensional computing (HDC), also known as Vector Symbolic Architecture, has gained significant attention for its hardware-efficient and accurate cognitive processing capabilities. By leveraging high-dimensional vector representations (hypervectors-HVs), HDC enables lightweight, single-pass learning. However, efficient and dynamic HV generation remains a key challenge, particularly for fully online learning in edge Al applications. Most existing approaches rely on pseudo-random, offline-generated HVs, which are neither adaptive nor software-independent, limiting their practicality in scenarios with varying data sizes, such as multi-resolution image processing. This work introduces three key innovations to advance online HDC for edge Al. First, we propose a lightweight, dynamic HV generator that operates entirely on-chip, eliminating the need for pre-generated vectors. Second, we introduce Gaussian Polygon, a novel multi-scale learning mechanism inspired by the Gaussian Pyramid, which performs Gaussian-like interpolation directly in binary HVs, achieving high efficiency without traditional upscaling techniques. Third, we show how Gaussian Polygon learning enables dynamic adaptation in HDC without conventional retraining mechanisms. Our hardware implementation in 45nm technology demonstrates up to 490 × reductions in power consumption and 676 × in hardware area, establishing the proposed framework as a practical and scalable solution for real-time edge learning.

Abnormal growth of cells in the brain and its surrounding tissues is known as a brain tumor. There are two types, one is benign (non-cancerous) and another is malignant (cancerous) which may cause death. The radiologists' ability to diagnose malignancies is greatly aided by magnetic resonance imaging (MRI). Brain cancer diagnosis has been considerably expedited by the field of computer-assisted diagnostics, especially in machine learning and deep learning. In our study, we categorize three different kinds of brain tumors using four transfer learning techniques. Our models were tested on a benchmark dataset of 3064 MRI pictures representing three different forms of brain cancer. Notably, ResNet-50 outperformed other models with a remarkable accuracy of 99.06%. We stress the significance of a balanced dataset for improving accuracy without the use of augmentation methods. Additionally, we experimentally demonstrate our method and compare with other classification algorithms on the CE-MRI dataset using evaluations like F1-score, AUC, precision and recall.

The collaboration between quantum computing and classical machine learning offers potential advantages in natural language processing, particularly in the sentiment analysis of human emotions and opinions expressed in large-scale datasets. In this work, we propose a methodology for sentiment analysis using hybrid quantum-classical machine learning algorithms. We investigate quantum kernel approaches and variational quantum circuit-based classifiers and integrate them with classical dimension reduction techniques such as PCA and Haar wavelet transform. The proposed methodology is evaluated using two distinct datasets, based on English and Bengali languages. Experimental results show that after dimensionality reduction of the data, performance of the quantum-based hybrid algorithms were consistent and better than classical methods.

An AI chatbot provides an impressive response after learning from the trained dataset. In this decade, most of the research work demonstrates that deep neural models superior to any other model. RNN model regularly used for determining the sequence-related problem like a question and it answers. This approach acquainted with everyone as seq2seq learning. In a seq2seq model mechanism, it has encoder and decoder. The encoder embedded any input sequence, and the decoder embedded output sequence. For reinforcing the seq2seq model performance, attention mechanism added into the encoder and decoder. After that, the transformer model has introduced itself as a high-performance model with multiple attention mechanism for solving the sequence-related dilemma. This model reduces training time compared with RNN based model and also achieved state-of-the-art performance for sequence transduction. In this research, we applied the transformer model for Bengali general knowledge chatbot based on the Bengali general knowledge Question Answer (QA) dataset. It scores 85.0 BLEU on the applied QA data. To check the comparison of the transformer model performance, we trained the seq2seq model with attention on our dataset that scores 23.5 BLEU.