44 citations · 75 across the 18 of their papers we have counts for
22 papers
Compiling Semi-Ring Dynamic Programming to Tier-Aware 3D-DRAM Processing-in-Memory
Mahbod Afarin, Tsung-Han Lu, Tajana Rosing
Processing-in-Memory (PIM) on monolithic 3D (M3D) DRAM is a promising answer to the memory wall for data-intensive dynamic programming (DP), yet extracting its performance today de…
Direct Observation of Nanoscale Polarization Switching in HZO Ferroelectrics Using STEM-EBIC
Prasanna Venkatesan, Taeyoung Song, Yueyun Chen +22
We report the experimental observation of discrete ferroelectric switching down to 0.475 nm x 0.475 nm in HfO2-based ferroelectrics using correlated STEM electron beam induced curr…
Cross-Domain Acceleration of Open Modification Search: From Commodity Platforms to Emerging Memory and Storage Devices
Sumukh Pinge, Chang Eun Song, Po-Kai Hsu +10
Open modification search (OMS) in mass spectrometry (MS) is a data-intensive workload whose performance is dominantly limited by reference data movement rather than computation. Pr…
D-NOVA: In-Storage Retrieval Accelerator via Dual-Bound 3D NAND-Optimized Similarity Search with Vector Adaptation
Chang Eun Song, Sumukh Pinge, Tianqi Zhang +3
Retrieval-Augmented Generation (RAG) enhances the factual grounding of large language model (LLM) inference by retrieving relevant information from external knowledge bases. Howeve…
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…
PIM-FW: Hardware-Software Co-Design of All-pairs Shortest Paths in DRAM
Tsung-Han Lu, Zheyu Li, Minxuan Zhou +1
All-pairs shortest paths (APSP) is a fundamental algorithm used for routing, logistics, and network analysis, but the cubic time complexity and heavy data movement of the canonical…
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…
FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
You Hak Lee, Xiaofan Yu, Keming Fan +4
Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challenging due to non-IID data, high…
SpecPCM: A Low-power PCM-based In-Memory Computing Accelerator for Full-stack Mass Spectrometry Analysis
Keming Fan, Ashkan Moradifirouzabadi, Xiangjin Wu +6
Mass spectrometry (MS) is essential for proteomics and metabolomics but faces impending challenges in efficiently processing the vast volumes of data. This paper introduces SpecPCM…
Fast-OverlaPIM: A Fast Overlap-driven Mapping Framework for Processing In-Memory Neural Network Acceleration
Xuan Wang, Minxuan Zhou, Tajana Rosing
Processing in-memory (PIM) is promising to accelerate neural networks (NNs) because it minimizes data movement and provides large computational parallelism. Similar to machine lear…
Efficient Open Modification Spectral Library Searching in High-Dimensional Space with Multi-Level-Cell Memory
Keming Fan, Wei-Chen Chen, Sumukh Pinge +2
Open Modification Search (OMS) is a promising algorithm for mass spectrometry analysis that enables the discovery of modified peptides. However, OMS encounters challenges as it exp…
Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing
Xiaofan Yu, Anthony Thomas, Ivannia Gomez Moreno +2
On-device learning has emerged as a prevailing trend that avoids the slow response time and costly communication of cloud-based learning. The ability to learn continuously and inde…
Federated Hyperdimensional Computing
Kazim Ergun, Rishikanth Chandrasekaran, Tajana Rosing
Federated learning (FL) enables a loose set of participating clients to collaboratively learn a global model via coordination by a central server and with no need for data sharing.…
Proxima: Near-storage Acceleration for Graph-based Approximate Nearest Neighbor Search in 3D NAND
Weihong Xu, Junwei Chen, Po-Kai Hsu +5
Approximate nearest neighbor search (ANNS) plays an indispensable role in a wide variety of applications, including recommendation systems, information retrieval, and semantic sear…
FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption
Minxuan Zhou, Yujin Nam, Pranav Gangwar +7
Fully Homomorphic Encryption (FHE) is a technique that allows arbitrary computations to be performed on encrypted data without the need for decryption, making it ideal for securing…
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…
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
Xiaofan Yu, Ludmila Cherkasova, Harsh Vardhan +5
Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deployi…
Trustworthy AI Inference Systems: An Industry Research View
Rosario Cammarota, Matthias Schunter, Anand Rajan +26
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems…
Prive-HD: Privacy-Preserved Hyperdimensional Computing
Behnam Khaleghi, Mohsen Imani, Tajana Rosing
The privacy of data is a major challenge in machine learning as a trained model may expose sensitive information of the enclosed dataset. Besides, the limited computation capabilit…
FPGA Acceleration of Sequence Alignment: A Survey
Sahand Salamat, Tajana Rosing
Genomics is changing our understanding of humans, evolution, diseases, and medicines to name but a few. As sequencing technology is developed collecting DNA sequences takes less ti…
FPGA Energy Efficiency by Leveraging Thermal Margin
Behnam Khaleghi, Sahand Salamat, Mohsen Imani +1
Cutting edge FPGAs are not energy efficient as conventionally presumed to be, and therefore, aggressive power-saving techniques have become imperative. The clock rate of an FPGA-ma…
QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning
Samuel Bosch, Alexander Sanchez de la Cerda, Mohsen Imani +2
Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a…