Representing complex structured data often involves a tradeoff between computational cost and representational quality, largely determined by the underlying encoding framework. Hyperdimensional Computing (HDC), also known as Vector Symbolic Architecture (VSA), has been recognized for its ability to represent complex structures through high-dimensional encoding. Conventional representation techniques in HDC, which in many cases rely on the cognitive operation of bundling for data memorization, suffer from quality and capacity limitations. Association through the binding operation offers a potential alternative, but its use is complicated by the difficult decomposition process, where classical factorization methods, such as resonator networks, can become challenging as the number of candidate factors and bound components increases. In this work, we explore a novel approach based on a quantum factorization algorithm inspired by Grover’s quantum search algorithm within HDC. This approach not only addresses the complexity challenges inherent in the decomposition of bound HDC representations, but also significantly mitigates noise and improves representational fidelity. Focusing on graph data structures, our framework Quark is analyzed through theoretical derivations and classical simulations of the quantum decoding process, demonstrating improved graph reconstruction quality across different graph sizes and dimensionalities. This integration of quantum computing and HDC provides a promising direction for scalable and high-fidelity graph representation.

Many computer vision applications rely on interpretable machine learning algorithms to analyze the data collected from various sources. We leverage Hyperdimensional Computing (HDC) as an innovative computational model that mimics key brain functionalities to achieve efficient and robust cognitive learning. We propose HDlm, a novel HDC-based cognitive representation capable of adaptive information association and memorization. HDlm first theoretically expands the HDC mathematics to support selective information association and adaptive memorization. Then, it exploits the proposed operations to support cognitive operations, including set membership, information retrieval, and item comparison. We evaluated our solution for a selection of applications related to visual data representation and sequence matching analysis. Our evaluation shows that HDlm provides more adaptive similarity metrics between objects that lead to better task performance.

This paper addresses the decomposition of holographic feature vectors in Hyperdimensional Computing (HDC) aka Vector Symbolic Architectures (VSA). HDC uses high-dimensional vectors with brain-like properties to represent symbolic information, and leverages efficient operators to construct and manipulate complexly structured data in a cognitive fashion. Existing models face challenges in de-composing these structures, a process crucial for under-standing and interpreting a composite hypervector. We ad-dress this challenge by proposing the HDC Memorized-Factorization Problem that captures the common patterns of construction in HDC models. To solve this problem efficiently, we introduce HDQMF, a HyperDimensional Quantum Memorized-Factorization algorithm. HDQMF is unique in its approach, utilizing quantum computing to of-fer efficient solutions. It modifies crucial steps in Grover's algorithm to achieve hypervector decomposition, achieving quadratic speed-up.

In machine learning (ML), near-sensor AI is transforming edge computing by reducing response times and data transmission, ultimately saving energy and bandwidth. Despite challenges like limited computational resources and the need for transparent decision-making, this approach aims to enhance the intelligence and autonomy of edge devices. Our research presents a novel framework that adds a layer of abstract intelligence to sensors, boosting system efficiency and accuracy through transparent, interpretable sub-symbolic AI. We combine Bayesian algorithms with hyperdimensional computing (HDC), inspired by the human brain's operational efficiency, to deliver an energy-efficient solution matching the accuracy of traditional cloud systems without constant server dependence. This framework uses a binary classifier with Bayesian insights to choose the best data processing location---locally or in the cloud---adapting to data environments. Our method ensures cloud-level performance while significantly reducing energy consumption, improving the sustainability of sensor-based systems. It also enables continual adaptation and learning directly at the sensor level, enriching cloud models with fresh edge insights. Our results have shown to bridge the gap from around 38% quality loss between the standalone near-sensor HDC model and the SOTA cloud-based model to improve the quality loss to only 9% while simultaneously saving 45.34% of energy by not using the cloud. This framework paves the way for more sustainable, efficient, and accurate edge computing in the ML landscape by bridging the gap between simple near-sensor models and their advanced cloud-based counterparts.

Recent advancement in emerging brain-inspired computing has pointed out a promising path to Machine Learning (ML) algorithms with high efficiency. Particularly, research in the field of HyperDimensional Computing (HDC) brings orders of magnitude speedup to both ML model training and inference compared to their deep learning counterparts. However, current HDC-based ML algorithms generally lack uncertainty estimation, despite having shown good results in various practical applications and outstanding energy efficiency. On the other hand, existing solutions such as the Bayesian Neural Networks (BNN) are generally much slower than regular neural networks and lead to high energy consumption. In this paper, we propose a hyperdimensional Bayesian framework called DiceHD, which enables uncertainty estimation for the HDC-based regression algorithm. The core of our framework is a specially designed HDC encoder that maps input features to the high dimensional space with an extra layer of randomness, i.e., a small number of dimensions are randomly dropped for each input. Our key insight is that by using this encoder, DiceHD implements Bayesian inference while maintaining the efficiency advantage of HDC. We verify our framework with both toy regression tasks and real-world datasets. We compare our DiceHD to several widely-used BNN baselines in terms of performance and efficiency. The results on CPU show that DiceHD provides comparable uncertainty estimations while achieving significant speedup compared to the BNN baseline. We also deploy DiceHD on two FPGA platforms with different acceleration capabilities, showing that DiceHD provides up to 84× (3740×) better energy efficiency for training (inference).

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.

Today's machine learning platforms have major robustness issues dealing with insecure and unreliable memory systems. In conventional data representation, bit flips due to noise or attack can cause value explosion, which leads to incorrect learning prediction. In this paper, we propose RobustHD, a robust and noise-tolerant learning system based on HyperDimensional Computing (HDC), mimicking important brain functionalities. Unlike traditional binary representation, RobustHD exploits a redundant and holographic representation, ensuring all bits have the same impact on the computation. RobustHD also proposes a runtime framework that adaptively identifies and regenerates the faulty dimensions in an unsupervised way. Our solution not only provides security against possible bit-flip attacks but also provides a learning solution with high robustness to noises in the memory. We performed a cross-stacked evaluation from a conventional platform to emerging processing in-memory architecture. Our evaluation shows that under 10% random bit flip attack, RobustHD provides a maximum of 0.53% quality loss, while deep learning solutions are losing over 26.2% accuracy.

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-the-art pattern matching algorithm running on GeForce RTX 3060 Ti GPU (state-of-the-art PIM accelerator).

Hyperdimensional Computing (HDC) is a neurally-inspired computation model working based on the observation that the human brain operates on high-dimensional representations of data, called hypervector. Although HDC is significantly powerful in reasoning and association of the abstract information, it is weak on features extraction from complex data such as image/video. As a result, most existing HDC solutions rely on expensive pre-processing algorithms for feature extraction. In this paper, we propose StocHD, a novel end-to-end hyperdimensional system that supports accurate, efficient, and robust learning over raw data. Unlike prior work that used HDC for learning tasks, StocHD expands HDC functionality to the computing area by mathematically defining stochastic arithmetic over HDC hypervectors. StocHD enables an entire learning application (including feature extractor) to process using HDC data representation, enabling uniform, efficient, robust, and highly parallel computation. We also propose a novel fully digital and scalable Processing In-Memory (PIM) architecture that exploits the HDC memory-centric nature to support extensively parallel computation. Our evaluation over a wide range of classification tasks shows that StocHD provides, on average, 3.3x and 6.4x (52.3x and 143.Sx) faster and higher energy efficiency as compared to state-of-the-art HDC algorithm running on PIM (NVIDIA GPU), while providing 16x higher computational robustness.

Pattern matching is one of the key algorithms in identifying and analyzing genomic data. In this paper, we propose HYPERS, a novel framework supporting highly efficient and parallel pattern matching based on HyperDimensional computing (HDC). HYPERS transforms inherent sequential processes of pattern matching to highly-parallelizable computation tasks using HDC. HYPERS exploits 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 matching, HYPERS performs alignment by exact or approximate similarity check of an encoded query with the HDC reference library. HYPERS functionality is supported by theoretical proof, verified by software implementation, and extensively tested on the existing hardware platform. Our evaluation on FPGA shows that HYPERS provides, on average, $ 17.5\times$ speedup and $ 39.4\times$ energy efficiency as compared to the state-of-the-art pattern matching tools running on GTX 1080 GPU.