[HTML][HTML] A survey on processing-in-memory techniques: Advances and challenges
Abstract Processing-in-memory (PIM) techniques have gained much attention from computer
architecture researchers, and significant research effort has been invested in exploring and …
architecture researchers, and significant research effort has been invested in exploring and …
A modern primer on processing in memory
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 …
design choice goes directly against at least three key trends in computing that cause …
Benchmarking a new paradigm: Experimental analysis and characterization of a real processing-in-memory system
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
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 …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
DAMOV: A new methodology and benchmark suite for evaluating data movement bottlenecks
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 …
ing performance, scalability, and energy efficiency in modern systems. Computer systems …
FPGA-based near-memory acceleration of modern data-intensive applications
Modern data-intensive applications demand high computational capabilities with strict
power constraints. Unfortunately, such applications suffer from a significant waste of both …
power constraints. Unfortunately, such applications suffer from a significant waste of both …
Parallel and distributed graph neural networks: An in-depth concurrency analysis
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 …
routinely solve complex problems on unstructured networks, such as node classification …
Benchmarking memory-centric computing systems: Analysis of real processing-in-memory hardware
Many modern workloads such as neural network inference and graph processing are
fundamentally memory-bound. For such workloads, data movement between memory and …
fundamentally memory-bound. For such workloads, data movement between memory and …
Sparsep: Towards efficient sparse matrix vector multiplication on real processing-in-memory architectures
Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …
Towards efficient sparse matrix vector multiplication on real processing-in-memory architectures
Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …