46 citations · 101 across the 18 of their papers we have counts for
18 papers
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…
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…
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…
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…
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…
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System
Yintao He, Haiyu Mao, Christina Giannoula +6
Large language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies on a time-consuming step called LLM decoding to generate ou…
Ariadne: A Hotness-Aware and Size-Adaptive Compressed Swap Technique for Fast Application Relaunch and Reduced CPU Usage on Mobile Devices
Yu Liang, Aofeng Shen, Chun Jason Xue +9
Growing application memory demands and concurrent usage are making mobile device memory scarce. When memory pressure is high, current mobile systems use a RAM-based compressed swap…
MegIS: High-Performance, Energy-Efficient, and Low-Cost Metagenomic Analysis with In-Storage Processing
Nika Mansouri Ghiasi, Mohammad Sadrosadati, Harun Mustafa +9
Metagenomics has led to significant advances in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative…
Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors
Taha Shahroodi, Gagandeep Singh, Mahdi Zahedi +6
Basecalling, an essential step in many genome analysis studies, relies on large Deep Neural Networks (DNNs) to achieve high accuracy. Unfortunately, these DNNs are computationally…
MetaStore: High-Performance Metagenomic Analysis via In-Storage Computing
Nika Mansouri Ghiasi, Mohammad Sadrosadati, Harun Mustafa +9
Metagenomics has led to significant advancements in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relat…
Venice: Improving Solid-State Drive Parallelism at Low Cost via Conflict-Free Accesses
Rakesh Nadig, Mohammad Sadrosadati, Haiyu Mao +6
The performance and capacity of solid-state drives (SSDs) are continuously improving to meet the increasing demands of modern data-intensive applications. Unfortunately, communicat…
RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes
Can Firtina, Nika Mansouri Ghiasi, Joel Lindegger +4
Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opport…
NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators
Aditya Manglik, Minesh Patel, Haiyu Mao +4
Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support highly-para…
GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping
Haiyu Mao, Mohammed Alser, Mohammad Sadrosadati +6
Nanopore sequencing is a widely-used high-throughput genome sequencing technology that can sequence long fragments of a genome into raw electrical signals at low cost. Nanopore seq…
Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis
Mohammed Alser, Joel Lindegger, Can Firtina +5
We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enoug…
GenStore: A High-Performance and Energy-Efficient In-Storage Computing System for Genome Sequence Analysis
Nika Mansouri Ghiasi, Jisung Park, Harun Mustafa +11
Read mapping is a fundamental, yet computationally-expensive step in many genomics applications. It is used to identify potential matches and differences between fragments (called…
Tackling Variabilities in Autonomous Driving
Yuqiong Qi, Yang Hu, Haibin Wu +5
The state-of-the-art driving automation system demands extreme computational resources to meet rigorous accuracy and latency requirements. Though emerging driving automation comput…