9 citations · 13 across the 7 of their papers we have counts for
12 papers
HDLouvain: High-Throughput Incremental Clustering for Hyperdimensional Data
Beenish Gul, Mahbod Afarin, Farhat Ahmed +4
Modern shotgun proteomics generates tens of thousands of tandem mass spectra per hour, producing hundreds of gigabytes of data that strain conventional clustering pipelines. DBSCAN…
Online Open-World Wafer Defect Discovery using Hyperdimensional Computing
Quanling Zhao, Ava Emami, Tsuyoshi Idé +4
Conventional wafer defect analysis relies on offline, closed-world classifiers that fail to detect novel, unseen defect signatures emerging during continuous semiconductor manufact…
Multi-bit Ferroelectric-NAND for High-throughput Massive Database Search
Prasanna Venkatesan, Tanvir H. Pantha, Po-Kai Hsu +28
The growing demand for large-scale database search in data-intensive applications, ranging from proteomics to autonomous systems, has exposed fundamental limitations in von Neumann…
Superposed Inference for Hyperdimensional Computing
Quanling Zhao, Nilesh Prasad Pandey, Ye Tian +1
Hyperdimensional computing (HDC) is attractive for efficient and robust learning, but conventional inference still encodes every query independently, repeatedly paying the cost of…
Superposed Latent Autoencoder
Quanling Zhao, Jiaying Yang, Tianqi Zhang +4
Autoencoders typically meet tight latent-memory budgets by making each latent representation smaller, sacrificing representational capacity. We ask a different question: can multip…
Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method
Quanling Zhao, Anthony Hitchcock Thomas, Ari Brin +2
Hyperdimensional computing (HDC) is an approach from the cognitive science literature for solving information processing tasks using data represented as high-dimensional random vec…
ACRONYM: Accelerated Approximate Nearest Neighbor Search in Memory for Dynamic Vector Databases
Md Mizanur Rahaman Nayan, Tianqi Zhang, Flavio Ponzina +2
Vector database search with frequent updates is increasingly critical in applications such as retrieval augmented generation, recommendation systems, and large-scale embedding retr…
MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems
Flavio Ponzina, Tajana Rosing
Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements.…
Mem-Rec: Memory Efficient Recommendation System using Alternative Representation
Gopi Krishna Jha, Anthony Thomas, Nilesh Jain +3
Deep learning-based recommendation systems (e.g., DLRMs) are widely used AI models to provide high-quality personalized recommendations. Training data used for modern recommendatio…
Massively Parallel Open Modification Spectral Library Searching with Hyperdimensional Computing
Jaeyoung Kang, Weihong Xu, Wout Bittremieux +1
Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain…
RAPIDx: High-performance ReRAM Processing in-Memory Accelerator for Sequence Alignment
Weihong Xu, Saransh Gupta, Niema Moshiri +1
Genome sequence alignment is the core of many biological applications. The advancement of sequencing technologies produces a tremendous amount of data, making sequence alignment a…
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge
Xiaofan Yu, Yunhui Guo, Sicun Gao +1
Unsupervised lifelong learning refers to the ability to learn over time while memorizing previous patterns without supervision. Although great progress has been made in this direct…