[HTML][HTML] From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures

M Alser, J Lindegger, C Firtina, N Almadhoun… - Computational and …, 2022‏ - Elsevier
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 …

Resource-efficient convolutional networks: A survey on model-, arithmetic-, and implementation-level techniques

JK Lee, L Mukhanov, AS Molahosseini… - ACM Computing …, 2023‏ - dl.acm.org
Convolutional neural networks (CNNs) are used in our daily life, including self-driving cars,
virtual assistants, social network services, healthcare services, and face recognition, among …

Benchmarking a new paradigm: Experimental analysis and characterization of a real processing-in-memory system

J Gómez-Luna, I El Hajj, I Fernandez… - IEEE …, 2022‏ - ieeexplore.ieee.org
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …

A modern primer on processing in memory

O Mutlu, S Ghose, J Gómez-Luna… - … computing: from devices …, 2022‏ - Springer
Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

SIMDRAM: A framework for bit-serial SIMD processing using DRAM

N Ha**azar, GF Oliveira, S Gregorio… - Proceedings of the 26th …, 2021‏ - dl.acm.org
Processing-using-DRAM has been proposed for a limited set of basic operations (ie, logic
operations, addition). However, in order to enable full adoption of processing-using-DRAM …

DAMOV: A new methodology and benchmark suite for evaluating data movement bottlenecks

GF Oliveira, J Gómez-Luna, L Orosa, S Ghose… - IEEE …, 2021‏ - ieeexplore.ieee.org
Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …

Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems

M Besta, R Kanakagiri, G Kwasniewski… - MICRO-54: 54th Annual …, 2021‏ - dl.acm.org
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Benchmarking a new paradigm: An experimental analysis of a real processing-in-memory architecture

J Gómez-Luna, IE Hajj, I Fernandez… - arxiv preprint arxiv …, 2021‏ - arxiv.org
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …

Parallel and distributed graph neural networks: An in-depth concurrency analysis

M Besta, T Hoefler - IEEE Transactions on Pattern Analysis and …, 2024‏ - ieeexplore.ieee.org
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Benchmarking memory-centric computing systems: Analysis of real processing-in-memory hardware

J Gómez-Luna, I El Hajj, I Fernandez… - 2021 12th …, 2021‏ - ieeexplore.ieee.org
Many modern workloads such as neural network inference and graph processing are
fundamentally memory-bound. For such workloads, data movement between memory and …