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 …
Benchmarking a new paradigm: An experimental analysis of a real processing-in-memory architecture
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 …
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 …
DRAM bender: An extensible and versatile FPGA-based infrastructure to easily test state-of-the-art DRAM chips
To understand and improve DRAM performance, reliability, security, and energy efficiency,
prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art …
prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art …
Evaluating machine learningworkloads on memory-centric computing systems
Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …
frequently memory-bound due to repeatedly accessing large training datasets. As a result …
An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System
Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …
frequently memory-bound due to repeatedly accessing large training datasets. As a result …
Machine learning training on a real processing-in-memory system
Machine learning (ML) algorithms [1]–[6] have become ubiquitous in many fields of science
and technology due to their ability to learn from and improve with experience with minimal …
and technology due to their ability to learn from and improve with experience with minimal …
Accelerating time series analysis via processing using non-volatile memories
Time Series Analysis (TSA) is a critical workload for consumer-facing devices. Accelerating
TSA is vital for many domains as it enables the extraction of valuable information and predict …
TSA is vital for many domains as it enables the extraction of valuable information and predict …
Sparsep: Efficient sparse matrix vector multiplication on real processing-in-memory architectures
C Giannoula, I Fernandez… - 2022 IEEE Computer …, 2022 - ieeexplore.ieee.org
Sparse Matrix Vector Multiplication (SpMV) is one of the most thoroughly studied scientific
computation kernels, be-cause it lies at the heart of many important applications from the …
computation kernels, be-cause it lies at the heart of many important applications from the …