60 citations · 222 across the 56 of their papers we have counts for
62 papers
HULK: An Efficient Algorithm for Enabling Fast Bulk Bitwise Operations on Homomorphically Encrypted Data
Mayank Kabra, Phillip Widdowson, Harshita Gupta +9
A wide range of important cloud applications, e.g., database analytics, graph analytics, and genome analysis, spend a considerable amount of time performing bulk bitwise operations…
PipeDRAM: A Data-Transposition-Free Processing-Using-DRAM Architecture with Hardware/Software Pipelining
Geraldo F. Oliveira, Ataberk Olgun, Ismail Emir Yüksel +6
Processing-using-DRAM (PUD) architectures exploit the analog operational properties of DRAM to perform bulk bitwise Boolean and arithmetic operations inside memory arrays by organi…
CertiFlash: A Formal Verification Framework for Flash Translation Layers in Computational Solid State Drives
Harshita Gupta, Mayank Kabra, Rakesh Nadig +10
Data-intensive applications move large amounts of data from storage to the compute unit, incurring significant data movement overhead. Storage-centric computing reduces this overhe…
FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration
Geraldo F. Oliveira, Arash Tavakkol, Xiangyu Zhu +10
LLM inference is increasingly constrained by accelerator memory capacity rather than compute throughput. This constraint is especially acute in single-accelerator and small-node in…
GRAINS: Storage-Aware Algorithm-Architecture Co-Design Enabling High-Performance and Low-Cost Graph-Based Genome Analysis
Nika Mansouri Ghiasi, Harun Mustafa, Talu Güloglu +8
Graph-based representations of genome sequences have emerged as a powerful approach for representing massive genomic databases in an expressive and efficient way. Despite their ben…
Clutch: High Performance Vector-Scalar Comparison using DRAM via Chunked Temporal Coding
Daichi Tokuda, Tatsuya Kubo, Ismail Emir Yuksel +8
Vector-scalar comparison is a fundamental computation primitive that compares each element in a vector against a single scalar value. It is widely used in various data-intensive wo…
ScaleDisturb: Exploiting Temporal Asymmetry to Amplify Read Disturbance in Modern DRAM Chips
Jikun Wang, Haocong Luo, Ataberk Olgun +6
DRAM suffers from read disturbance phenomena (e.g., RowHammer and RowPress), where repeatedly accessing or continuously keeping open a DRAM row (aggressor row) induces bitflips in…
Taking Cryptography Out of the Data Path via Near-Memory Processing in DRAM
Nicola Barcarolo, Brahmaiah Gandham, Mohammad Sadrosadati +3
Cryptographic algorithms such as AES-128 and SHA-256 are fundamental to ensuring data security and integrity. Although these algorithms are computationally efficient, their perform…
HE-PIM: Demystifying Homomorphic Operations on a Real-world Processing-in-Memory System
Harshita Gupta, Mayank Kabra, Jaewoo Park +9
Homomorphic encryption (HE) enables computation over encrypted data, offering strong privacy guarantees for untrusted computing environments. Practical adoption remains limited by…
ALPHA-PIM: Analysis of Linear Algebraic Processing for High-Performance Graph Applications on a Real Processing-In-Memory System
Marzieh Barkhordar, Alireza Tabatabaeian, Mohammad Sadrosadati +5
Processing large-scale graph datasets is computationally intensive and time-consuming. Processor-centric CPU and GPU architectures, commonly used for graph applications, often face…
GenPairX: A Hardware-Algorithm Co-Designed Accelerator for Paired-End Read Mapping
Julien Eudine, Chu Li, Zhuo Cheng +11
Genome sequencing has become a central focus in computational biology. A genome study typically begins with sequencing, which produces millions to billions of short DNA fragments k…
Conduit: Programmer-Transparent Near-Data Processing Using Multiple Compute-Capable Resources in Solid State Drives
Rakesh Nadig, Vamanan Arulchelvan, Mayank Kabra +9
Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support three NDP paradigms: in-storage process…
Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning
Rahul Bera, Zhenrong Lang, Caroline Hengartner +4
Prefetching and off-chip prediction are two techniques proposed to hide long memory access latencies in high-performance processors. In this work, we demonstrate that: (1) prefetch…
DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures
Peiming Yang, Sankeerth Durvasula, Ivan Fernandez +4
High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large La…
In-DRAM True Random Number Generation Using Simultaneous Multiple-Row Activation: An Experimental Study of Real DRAM Chips
Ismail Emir Yuksel, Ataberk Olgun, F. Nisa Bostanci +5
In this work, we experimentally demonstrate that it is possible to generate true random numbers at high throughput and low latency in commercial off-the-shelf (COTS) DRAM chips by…
TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents
Lukas Breitwieser, Ahmad Hesam, Abdullah Giray Yağlıkçı +3
Agent-based simulation is a method for studying highly complex systems in disciplines as diverse as biology, medicine, sociology, economics, finance, and more. In this method, an a…
MARS: Processing-In-Memory Acceleration of Raw Signal Genome Analysis Inside the Storage Subsystem
Melina Soysal, Konstantina Koliogeorgi, Can Firtina +8
Raw signal genome analysis (RSGA) has emerged as a promising approach to enable real-time genome analysis by directly analyzing raw electrical signals. However, rapid advancements…
PuDHammer: Experimental Analysis of Read Disturbance Effects of Processing-using-DRAM in Real DRAM Chips
Ismail Emir Yuksel, Akash Sood, Ataberk Olgun +6
Processing-using-DRAM (PuD) is a promising paradigm for alleviating the data movement bottleneck using DRAM's massive internal parallelism and bandwidth to execute very wide operat…
REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing
Kangqi Chen, Andreas Kosmas Kakolyris, Rakesh Nadig +7
Large Language Models (LLMs) face an inherent challenge: their knowledge is confined to the data that they have been trained on. To overcome this issue, Retrieval-Augmented Generat…
Processing-in-memory for genomics workloads
William Andrew Simon, Leonid Yavits, Konstantina Koliogeorgi +12
Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the backbone of the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized,…
Accelerating Triangle Counting with Real Processing-in-Memory Systems
Lorenzo Asquini, Manos Frouzakis, Juan Gómez-Luna +3
Triangle Counting (TC) is a procedure that involves enumerating the number of triangles within a graph. It has important applications in numerous fields, such as social or biologic…
PIMDAL: Mitigating the Memory Bottleneck in Data Analytics using a Real Processing-in-Memory System
Manos Frouzakis, Juan Gómez-Luna, Geraldo F. Oliveira +2
Database Management Systems (DBMSs) are crucial for efficient data management and analytics, and are used in several different application domains. Due to the increasing volume of…
SAGe: A Lightweight Algorithm-Architecture Co-Design for Mitigating the Data Preparation Bottleneck in Large-Scale Genome Sequence Analysis
Nika Mansouri Ghiasi, Talu Güloglu, Harun Mustafa +8
Genome sequence analysis, which examines the DNA sequences of organisms, drives advances in many critical medical and biotechnological fields. Given its importance and the exponent…
CIPHERMATCH: Accelerating Homomorphic Encryption-Based String Matching via Memory-Efficient Data Packing and In-Flash Processing
Mayank Kabra, Rakesh Nadig, Harshita Gupta +7
Homomorphic encryption (HE) allows secure computation on encrypted data without revealing the original data, providing significant benefits for privacy-sensitive applications. Many…