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.

Graph analysis has emerged as a crucial field, offering versatile solutions for real-world data representation, from social networks to biological systems. However, the intricate nature of graphs often necessitates a degree of processing, such as learning mappings to a vector space, to perform analysis tasks like node classification and link prediction. A promising approach to this is Hyperdimensional Computing (HDC), inspired by neuroscience and mathematics. HDC utilizes high-dimensional vectors to efficiently manipulate complex data structures and perform operations like superposition and association, enhancing knowledge graph representations with contextual and semantic information. Nevertheless, addressing limitations in existing HDC-based approaches to graph representation is essential. This paper thoroughly explores these methods and presents ConfiGR: Configurable Graph Representation, a novel framework that introduces an adjustable design, enhancing its versatility across various graph types and tasks, ultimately boosting performance in multiple graph-related tasks.

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.

Integrating deep learning with Reinforcement Learning (RL) results in algorithms that achieve human-like learning in complex yet unknown environments via a process of trial and error. Despite the advancements, the computational costs associated with deep learning become a major drawback. This paper proposes a revamped Q-learning algorithm powered by Hyperdimensional Computing (HDC), targeting more efficient and adaptive exploration. We introduce a solution leveraging model uncertainty to navigate agent exploration. Our evaluation shows that the proposed algorithm is a significant enhancement in learning quality and efficiency compared to previous HDC-based algorithms, achieving more than 330 more rewards with small overheads in computation. In addition, it maintains an edge over DNN-based alternatives by ensuring reduced runtime costs and improved policy learning, achieving up to 6.9 × faster learning.

Novel computer architectures like Compute-in-Memory (CiM) merge the memory and processing units, mimicking the human brain. Simultaneously, Hyperdimensional Computing (HDC) is emerging as a brain-inspired machine learning (ML) approach. Both developments hold promise for the realm of AI and computing, especially for genome-matching tasks, where large data movements overwhelm traditional von Neumann architectures. FeFET is one of the up-and-coming emerging technologies that promises to enable ultra-efficient and compact CiM architectures. However, the adoption of FeFETs is hindered by their 10 nm-thick Ferroelectric (FE) layer and process variation. Thus, calculations with FeFETs have errors (noise) that traditional ML genome-matching models cannot tolerate. To overcome these challenges, this work is the first one to i) present a reliable HDC framework (HDGIM) for highly-scaled (down to merely 3nm), multi-bit FeFET technology, ii) introduce temperature-thickness modeled noise from FeFET to the HDC system, and iii) extensively define the memorization capacity of HDC hyperparameters in order to evaluate the performance before deployment theoretically. Our novel HDC learning framework iteratively uses two models: a full-precision 32-bit HDC model, an ideal model for training, and a reduced bit-precision by a novel quantization method for validation and inference. Our results demonstrate that highly-scaled FeFET, realizing 3-bit and even 4-bit, can withstand any modeled noise given high dimensionality during inference. Considering the noise during model adjustment improves the inherent robustness by almost 9% on the 4-bit case.

Brain-inspired Hyper-dimensional computing (HDC) has recently shown promise as a lightweight machine learning approach. Despite its success, there are limited studies on the robustness of HDC models to adversarial attacks. In this paper, we introduce the first comparative study of the robustness between HDC and deep neural network (DNN) to malicious attacks. We develop a framework that enables HDC models to generate gradient-based adversarial examples using state-of-the-art techniques applied to DNNs. Our evaluation shows that HDC with a proper neural encoding module provides significantly higher robustness to adversarial attacks than existing DNNs. In addition, HDC models have high robustness to adversarial samples generated for DNNs.

This is the first work to present a reliable application for highly scaled (down to merely 3nm), multi-bit Ferroelectric FET (FeFET) technology. FeFET is one of the up-and-coming emerging technologies that is not only fully compatible with the existing CMOS but does hold the promise to realize ultra-efficient and compact Compute-in-Memory (CiM) architectures. Nevertheless, FeFETs struggle with the 10nm thickness of the Ferroelectric (FE) layer. This makes scaling profoundly challenging if not impossible because thinner FE significantly shrinks the memory window leading to large error probabilities that cannot be tolerated. To overcome these challenges, we propose HDGIM, a hyperdimensional computing framework catered to FeFET in the context of genome sequence matching. Genome Sequence Matching is known to have high computational costs, primarily due to huge data movement that substantially overwhelms von-Neuman architectures. On the one hand, our cross-layer FeFET reliability modeling (starting from device physics to circuits) accurately captures the impact of FE scaling on errors induced by process variation and inherent stochasticity in multi-bit FeFETs. On the other hand, our HDC learning framework iteratively adapts by using two models, a full-precision, ideal model for training and a quantized, noisy version for validation and inference. Our results demonstrate that highly scaled FeFET realizing 3-bit and even 4-bit can withstand any noise given high dimensionality during inference. If we consider the noise during model adjustment, we can improve the inherent robustness compared to adding noise during the matching process.

Recent strides in deep learning have yielded impres-sive practical applications such as autonomous driving, natural language processing, and graph reasoning. However, the sus-ceptibility of deep learning models to subtle input variations, which stems from device imperfections and non-idealities, or adversarial attacks on edge devices, presents a critical challenge. These vulnerabilities hold dual significance-security concerns in critical applications and insights into human-machine sen-sory alignment. Efforts to enhance model robustness encounter resource constraints in the edge and the black box nature of neural networks, hindering their deployment on edge devices. This paper focuses on algorithmic adaptations inspired by the human brain to address these challenges. Hyper Dimensional Computing (HDC), rooted in neural principles, replicates brain functions while enabling efficient, noise-tolerant computation. HDC leverages high-dimensional vectors to encode information, seamlessly blending learning and memory functions. Its trans-parency empowers practitioners, enhancing both robustness and understanding of deployed models. In this paper, we introduce the first comprehensive study that compares the robustness of HDC to white-box malicious attacks to that of deep neural network (DNN) models and the first HDC gradient-based attack in the literature. We develop a framework that enables HDC models to generate gradient-based adversarial examples using state-of-the-art techniques applied to DNNs. Our evaluation shows that our HDC model provides, on average, 19.9% higher robustness than DNNs to adversarial samples and up to 90% robustness improvement against random noise on the weights of the model compared to the DNN.

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).

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.

Hyper-Dimensional (HD) computing emulates the human short memory functionality by computing with hyper-vectors as an alternative to computing with numbers. The main goal of HD computing is to map data points into sparse high-dimensional space where the learning task can perform in a linear and hardware-friendly way. The existing HD computing algorithms are using static and non-trainable encoder; thus, they require very high-dimensionality to provide acceptable accuracy. However, this high dimensionality results in high computational cost, especially over the realistic learning problems. In this paper, we proposed ManiHD that supports adaptive and trainable encoder for efficient learning in high-dimensional space. ManiHD explicitly considers non-linear interactions between the features during the encoding. This enables ManiHD to provide maximum learning accuracy using much lower dimensionality. ManiHD not only enhances the learning accuracy but also significantly improves the learning efficiency during both training and inference phases. ManiHD also enables online learning by sampling data points and capturing the essential features in an unsupervised manner. We also propose a quantization method that trades accuracy and efficiency for optimal configuration. Our evaluation of a wide range of classification tasks shows that ManiHD provides 4.8% higher accuracy than the state-of-the-art HD algorithms. In addition, ManiHD provides, on average, 12.3× (3.2×) faster and 19.3× (6.3×) more energy-efficient training (inference) as compared to the state-of-the-art learning algorithms.

Today's applications are using machine learning algorithms to analyze the data collected from a swarm of devices on the Internet of Things (IoT). However, most existing learning algorithms are overcomplex to enable real-time learning on IoT devices with limited resources and computing power. Recently, Hyperdimensional computing (HDC) is introduced as an alternative computing paradigm for enabling efficient and robust learning. HDC emulates the cognitive task by representing the values as patterns of neural activity in high-dimensional space. HDC first encodes all data points to high-dimensional vectors. It then efficiently performs the learning task using a well-defined set of operations. Existing HDC solutions have two main issues that hinder their deployments on low-power embedded devices: (i) the encoding module is costly, dominating 80% of the entire training performance, (ii) the HDC model size and the computation cost grow significantly with the number of classes in online inference.In this paper, we proposed a novel architecture, LookHD, which enables real-time HDC learning on low-power edge devices. LookHD exploits computation reuse to memorize the encoding module and simplify its computation with single memory access. LookHD also address the inference scalability by exploiting HDC governing mathematics that compresses the HDC trained model into a single hypervector. We present how the proposed architecture can be implemented on the existing low power architectures: ARM processor and FPGA design. We evaluate the efficiency of the proposed approach on a wide range of practical classification problems such as activity recognition, face recognition, and speech recognition. Our evaluations show that LookHD can achieve, on average, $ 28.3\times$ faster and $ 97.4\times$ more energy-efficient training as compared to the state-of-the-art HDC implemented on the FPGA. Similarly, in the inference, LookHD is $ 2.2\times$ faster, $ 4.1\times$ more energy-efficient, and has $ 6.3\times$ smaller model size than the same state-of-the-art algorithms.

Hyperdimensional Computing (HDC) is an emerging computational framework that mimics important brain functions by operating over high-dimensional vectors, called hypervectors (HVs). In-memory computing implementations of HDC are desirable since they can significantly reduce data transfer overheads. All existing in-memory HDC platforms consider binary HVs where each dimension is represented with a single bit. However, utilizing multi-bit HVs allows HDC to achieve acceptable accuracies in lower dimensions which in turn leads to higher energy efficiencies. Thus, we propose a highly accurate and efficient multi-bit in-memory HDC inference platform called MIMHD. MIMHD supports multi-bit operations using ferroelectric field-effect transistor (FeFET) crossbar arrays for multiply-and-add and FeFET multi-bit content-addressable memories for associative search. We also introduce a novel hardware-aware retraining framework (HWART) that trains the HDC model to learn to work with MIMHD. For six popular datasets and 4000 dimension HVs, MIMHD using 3-bit (2-bit) precision HVs achieves (i) average accuracies of 92.6% (88.9%) which is 8.5% (4.8%) higher than binary implementations; (ii) 84.1× (78.6×) energy improvement over a GPU, and (iii) 38.4×(34.3×) speedup over a GPU, respectively. The 3-bit MIMHD is 4.3× and 13× faster and more energy-efficient than binary HDC accelerators while achieving similar accuracies.

In the Internet of Things (IoT) domain, many applications are running machine learning algorithms to assimilate the data collected in the swarm of devices. Sending all data to the powerful computing environment, e.g., cloud, poses significant efficiency and scalability issues. A promising way is to distribute the learning tasks onto the IoT hierarchy, often referred to edge computing; however, the existing sophisticated algorithms such as deep learning are often overcomplex to run on less-powerful and unreliable embedded IoT devices. Hyperdimensional Computing (HDC) is a brain-inspired learning approach for efficient and robust learning on today's embedded devices. Encoding, or transforming the input data into high-dimensional representation, is the key first step of HDC before performing a learning task. All existing HDC approaches use a static encoder; thus, they still require very high dimensionality, resulting in significant efficiency loss for the edge devices with limited resources. In this paper, we have developed NeuralHD, a new HDC approach with a dynamic encoder for adaptive learning. Inspired by human neural regeneration study in neuroscience, NeuralHD identifies insignificant dimensions and regenerates those dimensions to enhance the learning capability and robustness. We also present a scalable learning framework to distribute NeuralHD computation over edge devices in IoT systems. Our solution enables edge devices capable of real-time learning from both labeled and unlabeled data. Our evaluation on a wide range of practical classification tasks shows that NeuralHD provides 5.7X and 6.1X (12.3X and 14.1X) faster and more energy-efficient training compared to the HD-based algorithms (DNNs) running on the same platform. NeuralHD also provides 4.2X and 11.6X higher robustness to noise in the unreliable network and hardware of IoT environments as compared to DNNs.