124 citations · 472 across the 16 of their papers we have counts for
19 papers
Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning
Rakesh Nadig, Vamanan Arulchelvan, Rahul Bera +7
Modern high-performance computing (HPC) environments rely on hybrid storage systems (HSS) that combine multiple storage devices with diverse latency, bandwidth, endurance, and capa…
High-Performance Data Mapping for BNNs on PCM-based Integrated Photonics
Taha Shahroodi, Raphael Cardoso, Stephan Wong +3
State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to…
Rethinking the Producer-Consumer Relationship in Modern DRAM-Based Systems
Minesh Patel, Taha Shahroodi, Aditya Manglik +4
Generational improvements to commodity DRAM throughout half a century have long solidified its prevalence as main memory across the computing industry. However, overcoming today's…
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…
An In-Memory Architecture for High-Performance Long-Read Pre-Alignment Filtering
Taha Shahroodi, Michael Miao, Joel Lindegger +3
With the recent move towards sequencing of accurate long reads, finding solutions that support efficient analysis of these reads becomes more necessary. The long execution time req…
BCIM: Efficient Implementation of Binary Neural Network Based on Computation in Memory
Mahdi Zahedi, Taha Shahroodi, Stephan Wong +1
Applications of Binary Neural Networks (BNNs) are promising for embedded systems with hard constraints on computing power. Contrary to conventional neural networks with the floatin…
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
Can Firtina, Kamlesh Pillai, Gurpreet S. Kalsi +10
Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequen…
Demeter: A Fast and Energy-Efficient Food Profiler using Hyperdimensional Computing in Memory
Taha Shahroodi, Mahdi Zahedi, Can Firtina +4
Food profiling is an essential step in any food monitoring system needed to prevent health risks and potential frauds in the food industry. Significant improvements in sequencing t…
A Case for Transparent Reliability in DRAM Systems
Minesh Patel, Taha Shahroodi, Aditya Manglik +4
Today's systems have diverse needs that are difficult to address using one-size-fits-all commodity DRAM. Unfortunately, although system designers can theoretically adapt commodity…
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis
Can Firtina, Jisung Park, Mohammed Alser +8
Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their h…
Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning
Rahul Bera, Konstantinos Kanellopoulos, Anant V. Nori +3
Past research has proposed numerous hardware prefetching techniques, most of which rely on exploiting one specific type of program context information (e.g., program counter, cache…
pLUTo: Enabling Massively Parallel Computation in DRAM via Lookup Tables
João Dinis Ferreira, Gabriel Falcao, Juan Gómez-Luna +8
Data movement between the main memory and the processor is a key contributor to execution time and energy consumption in memory-intensive applications. This data movement bottlenec…
BurstLink: Techniques for Energy-Efficient Conventional and Virtual Reality Video Display
Jawad Haj-Yahya, Jisung Park, Rahul Bera +5
Conventional planar video streaming is the most popular application in mobile systems and the rapid growth of 360 video content and virtual reality (VR) devices are accelerating th…
BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows
Abdullah Giray Yağlıkçı, Minesh Patel, Jeremie S. Kim +9
Aggressive memory density scaling causes modern DRAM devices to suffer from RowHammer, a phenomenon where rapidly activating a DRAM row can cause bit-flips in physically-nearby row…
Bit-Exact ECC Recovery (BEER): Determining DRAM On-Die ECC Functions by Exploiting DRAM Data Retention Characteristics
Minesh Patel, Jeremie S. Kim, Taha Shahroodi +2
Increasing single-cell DRAM error rates have pushed DRAM manufacturers to adopt on-die error-correction coding (ECC), which operates entirely within a DRAM chip to improve factory…
CLR-DRAM: A Low-Cost DRAM Architecture Enabling Dynamic Capacity-Latency Trade-Off
Haocong Luo, Taha Shahroodi, Hasan Hassan +5
DRAM is the prevalent main memory technology, but its long access latency can limit the performance of many workloads. Although prior works provide DRAM designs that reduce DRAM ac…
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations
Konstantinos Kanellopoulos, Nandita Vijaykumar, Christina Giannoula +6
Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations. These operations use sparse matrix compression as…
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
Skanda Koppula, Lois Orosa, Abdullah Giray Yağlıkçı +4
The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference syst…
SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs
Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna +2
Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The…