A survey of coarse-grained reconfigurable architecture and design: Taxonomy, challenges, and applications
As general-purpose processors have hit the power wall and chip fabrication cost escalates
alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing …
alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing …
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 …
Pipelayer: A pipelined reram-based accelerator for deep learning
Convolution neural networks (CNNs) are the heart of deep learning applications. Recent
works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access …
works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access …
Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory
Processing-in-memory (PIM) is a promising solution to address the" memory wall"
challenges for future computer systems. Prior proposed PIM architectures put additional …
challenges for future computer systems. Prior proposed PIM architectures put additional …
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 …
Drisa: A dram-based reconfigurable in-situ accelerator
Data movement between the processing units and the memory in traditional von Neumann
architecture is creating the" memory wall" problem. To bridge the gap, two approaches, the …
architecture is creating the" memory wall" problem. To bridge the gap, two approaches, the …
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 …
SIMDRAM: A framework for bit-serial SIMD processing using DRAM
N Hajinazar, 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 …
operations, addition). However, in order to enable full adoption of processing-using-DRAM …
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 …