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 …
Ambit: In-memory accelerator for bulk bitwise operations using commodity DRAM technology
Many important applications trigger bulk bitwise operations, ie, bitwise operations on large
bit vectors. In fact, recent works design techniques that exploit fast bulk bitwise operations to …
bit vectors. In fact, recent works design techniques that exploit fast bulk bitwise operations to …
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 …
Google workloads for consumer devices: Mitigating data movement bottlenecks
We are experiencing an explosive growth in the number of consumer devices, including
smartphones, tablets, web-based computers such as Chromebooks, and wearable devices …
smartphones, tablets, web-based computers such as Chromebooks, and wearable devices …
Processing data where it makes sense: Enabling in-memory computation
Today's systems are overwhelmingly designed to move data to computation. This design
choice goes directly against at least three key trends in systems that cause performance …
choice goes directly against at least three key trends in systems that cause performance …
Rowhammer: A retrospective
This retrospective paper describes the RowHammer problem in dynamic random access
memory (DRAM), which was initially introduced by Kim et al. at the ISCA 2014 Conference …
memory (DRAM), which was initially introduced by Kim et al. at the ISCA 2014 Conference …
Recnmp: Accelerating personalized recommendation with near-memory processing
Personalized recommendation systems leverage deep learning models and account for the
majority of data center AI cycles. Their performance is dominated by memory-bound sparse …
majority of data center AI cycles. Their performance is dominated by memory-bound sparse …
Processing-in-memory: A workload-driven perspective
Many modern and emerging applications must process increasingly large volumes of data.
Unfortunately, prevalent computing paradigms are not designed to efficiently handle such …
Unfortunately, prevalent computing paradigms are not designed to efficiently handle such …
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 …
HRL: Efficient and flexible reconfigurable logic for near-data processing
M Gao, C Kozyrakis - 2016 IEEE International Symposium on …, 2016 - ieeexplore.ieee.org
The energy constraints due to the end of Dennard scaling, the popularity of in-memory
analytics, and the advances in 3D integration technology have led to renewed interest in …
analytics, and the advances in 3D integration technology have led to renewed interest in …