hyperdimensional computing
PulseAugur coverage of hyperdimensional computing — every cluster mentioning hyperdimensional computing across labs, papers, and developer communities, ranked by signal.
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Survey details hyperdimensional computing encoding methods
This paper provides a comprehensive survey of hyperdimensional computing (HDC), focusing specifically on the methods used to generate and encode data into hypervectors. HDC is an emerging paradigm that mimics the brain'…
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New research explores watermarking for hyperdimensional computing classifiers
Researchers have investigated model watermarking techniques for hyperdimensional computing (HDC) classifiers, adapting backdoor-based embedding to three distinct HDC models. The study found that error-driven classifiers…
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WaferHDC framework uses hyperdimensional computing for open-world defect discovery
Researchers have developed WaferHDC, a novel framework for identifying defects in semiconductor manufacturing using hyperdimensional computing. This system operates in an open-world setting, allowing it to detect new an…
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New framework improves accuracy of hyperdimensional computing codebooks
Researchers have developed a new framework to improve the accuracy of dense binary hypervector codebooks used in hyperdimensional computing and vector symbolic architectures. This framework, called transition-based dera…
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New OASIS framework slashes in-sensor vision data transmission costs
Researchers have developed OASIS, a novel framework for distributed in-sensor vision that significantly reduces data transmission costs by processing information near the image sensor. This system employs a lightweight …
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New BiHDTrans model merges hyperdimensional computing with Transformers for efficient time series classification
Researchers have developed BiHDTrans, a novel neurosymbolic binary hyperdimensional Transformer designed for efficient multivariate time series classification, particularly for resource-constrained edge environments. Th…
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New MMPerc classifiers offer improved performance over SVMs and standard Perceptrons
Researchers have introduced a new family of multiclass linear Perceptron classifiers called MMPerc, which utilize a multiplicative margin mechanism. This approach enhances classification confidence by ensuring the corre…
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New protocol aims to enhance LLM safety with design-time contracts
A research paper introduced Holographic Invariant Storage (HIS), a protocol designed to enhance LLM safety by mitigating context drift. HIS utilizes Vector Symbolic Architectures to establish design-time safety contract…
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New methods boost privacy and efficiency in decentralized federated learning · 2 papers tracked
Two new research papers introduce novel approaches to enhance privacy and efficiency in decentralized federated learning. The first paper, PrivateDFL, utilizes hyperdimensional computing and a transparent noise accounta…
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New NysHD method bridges hyperdimensional computing and kernel methods
Researchers have introduced NysHD, a novel method for mapping data into high-dimensional space within the framework of hyperdimensional computing (HDC). This new technique leverages the Nyström method, commonly used in …
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New Hyperdimensional Probe Decodes LLM Representations
Researchers have developed a new interpretability method called the Hyperdimensional Probe, which combines symbolic representations with neural probing to better understand the internal workings of Large Language Models…
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Quantum computing framework boosts hyperdimensional computing efficiency
Researchers have developed a new quantum computing framework designed to make hyperdimensional computing (HDC) more efficient. This approach significantly reduces the number of qubits required for hypervector representa…
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New AGI Theory Proposes Set Theory and Hyperdimensional Computing Foundation
A new computational theory of mind proposes a foundation for Artificial General Intelligence (AGI) based on set theory and hyperdimensional computing. This framework utilizes sparse binary data and discrete sets, diverg…
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New framework ApproxHDC optimizes Hyperdimensional Computing with compiler-driven approximations
Researchers have developed ApproxHDC, a novel framework that leverages compiler-driven approximation tuning to enhance the efficiency of Hyperdimensional Computing (HDC) workloads. This approach is designed to address t…
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New Quantum-Enhanced Hyperdimensional Computing Framework Introduced
Researchers have introduced QeHDC, a novel framework for Quantum-enhanced Hyperdimensional Computing. This approach utilizes sinusoidal and quantum encoding to project data into quantum states and employs a reference-st…
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Hillock AI debuts local memory system with SQLite and HDC
Hillock has developed a local AI memory system that utilizes SQLite and hyperdimensional computing. This system is designed to store and retrieve information without relying on cloud services, employing brain-inspired p…
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New Theory: "Impedance Mismatch" Hinders AI Fusion
A new theoretical paper introduces the concept of "Impedance Mismatch" to describe the fundamental division between foundation models and knowledge graphs. The authors argue that current methods like Retrieval-Augmented…
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New HDC Framework Enhances Anomaly Detection for Edge AI
Researchers have introduced D2H-AD, a novel anomaly detection framework that leverages Hyperdimensional Computing (HDC). This brain-inspired approach uses high-dimensional vectors to represent information, integrating d…
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Hyperdimensional Computing Enhances Structured Querying on Tabular Data
Researchers have explored the application of Hyperdimensional Computing (HDC), specifically the Holographic Reduced Representations (HRR) model, for structured querying on tabular data embeddings. This approach aims to …
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New memory architecture boosts AI symbolic reasoning efficiency
Researchers have introduced Orthogonal Subspace Carving (OSC), a novel memory architecture designed to enhance symbolic reasoning in AI models. OSC addresses the dimensionality issues of Tensor Product Representations b…