activity
20232026
most citedHD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

9 citations · 10 across the 10 of their papers we have counts for

collaborators

11 papers

cond-mat.mes-hall2026

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…

cs.AR2026

PALUTE: Processing-In-Memory Acceleration via Lookup Table for Edge LLM Inference

Runyang Tian, Yanru Chen, Weihong Xu +1

Large language models are increasingly deployed on edge devices with tight power and area budgets. While mixed-precision GEMM reduces arithmetic complexity, quantized inference is…

cs.AR2026

HAVEN: High-Bandwidth Flash Augmented Vector Engine for Large-Scale Approximate Nearest-Neighbor Search Acceleration

Po-Kai Hsu, Weihong Xu, Qunyou Liu +2

Retrieval-Augmented Generation (RAG) relies on large-scale Approximate Nearest Neighbor Search (ANNS) to retrieve semantically relevant context for large language models. Among ANN…

cs.AR2025

FSL-HDnn: A 40 nm Few-shot On-Device Learning Accelerator with Integrated Feature Extraction and Hyperdimensional Computing

Weihong Xu, Chang Eun Song, Haichao Yang +5

This paper introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction and on-device few-shot learning (FSL). The accelerator…

cs.AR2025

HDDB: Efficient In-Storage SQL Database Search Using Hyperdimensional Computing on Ferroelectric NAND Flash

Quanling Zhao, Yanru Chen, Runyang Tian +6

Hyperdimensional Computing (HDC) encodes information and data into high-dimensional distributed vectors that can be manipulated using simple bitwise operations and similarity searc…

cs.AR2025

FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash

Sumukh Pinge, Ashkan Moradifirouzabadi, Keming Fan +29

The rapid expansion of mass spectrometry (MS) data, now exceeding hundreds of terabytes, poses significant challenges for efficient, large-scale library search - a critical compone…

cs.AR2025

Clo-HDnn: A 4.66 TFLOPS/W and 3.78 TOPS/W Continual On-Device Learning Accelerator with Energy-efficient Hyperdimensional Computing via Progressive Search

Chang Eun Song, Weihong Xu, Keming Fan +10

Clo-HDnn is an on-device learning (ODL) accelerator designed for emerging continual learning (CL) tasks. Clo-HDnn integrates hyperdimensional computing (HDC) along with low-cost Kr…

cs.PL2024

HPVM-HDC: A Heterogeneous Programming System for Accelerating Hyperdimensional Computing

Russel Arbore, Xavier Routh, Abdul Rafae Noor +7

Hyperdimensional Computing (HDC), a technique inspired by cognitive models of computation, has been proposed as an efficient and robust alternative basis for machine learning. HDC…

cs.DC2024★ 1 cited

RapidOMS: FPGA-based Open Modification Spectral Library Searching with HD Computing

Sumukh Pinge, Weihong Xu, Wout Bittremieux +3

Mass spectrometry (MS) is essential for protein analysis but faces significant challenges with large datasets and complex post-translational modifications, resulting in difficultie…

cs.AR2024

FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing

Haichao Yang, Chang Eun Song, Weihong Xu +6

This paper introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction, classification, and on-chip few-shot learning (FSL) t…

q-bio.BM2023★ 9 cited

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Derek Jones, Jonathan E. Allen, Xiaohua Zhang +5

Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional m…