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Clustering-based data placement in cloud computing: a predictive approach
Nowadays, cloud computing environments have become a natural choice to host and
process a huge volume of data. The combination of cloud computing and big data …
process a huge volume of data. The combination of cloud computing and big data …
Downshift: Tuning shift reduction with reliability for racetrack memories
Ultra-dense non-volatile racetrack memories (RTMs) have been investigated at various
levels in the memory hierarchy for improved performance and reduced energy consumption …
levels in the memory hierarchy for improved performance and reduced energy consumption …
Polyhedral compilation for racetrack memories
Traditional memory hierarchy designs, primarily based on SRAM and DRAM, become
increasingly unsuitable to meet the performance, energy, bandwidth, and area requirements …
increasingly unsuitable to meet the performance, energy, bandwidth, and area requirements …
Optimizing data placement for hybrid sram+ racetrack memory spm in embedded systems
Nonvolatile memory (NVM) has the potential as the medium for scratchpad memory (SPM) in
embedded devices. Racetrack memory (RM), in particular, is a develo** memory …
embedded devices. Racetrack memory (RM), in particular, is a develo** memory …
ROLLED: Racetrack memory optimized linear layout and efficient decomposition of decision trees
Modern low power distributed systems tend to integrate machine learning algorithms. In
resource-constrained setups, the execution of the models has to be optimized for …
resource-constrained setups, the execution of the models has to be optimized for …
MASTER: Reclamation of hybrid scratchpad memory to maximize energy saving in multi-core edge systems
Most modern multi-core edge devices work in outdoor situations with limited power supplies
like energy harvester and batteries. Therefore, energy consumption is a fundamental issue …
like energy harvester and batteries. Therefore, energy consumption is a fundamental issue …
Optimizing tensor contractions for embedded devices with racetrack and dram memories
Tensor contraction is a fundamental operation in many algorithms with a plethora of
applications ranging from quantum chemistry over fluid dynamics and image processing to …
applications ranging from quantum chemistry over fluid dynamics and image processing to …
ERMES: Efficient racetrack memory emulation system based on FPGA
With the scaling of CMOS technology almost over, non-volatile memories based on
emerging technologies are gaining considerable popularity. Particularly, spintronic-based …
emerging technologies are gaining considerable popularity. Particularly, spintronic-based …
Optimizing Data Layout for Racetrack Memory in Embedded Systems
Racetrack memory (RTM), which consists of multiple domain block clusters (DBC) and
access ports, is a novel non-volatile memory and has potential as scratchpad memory (SPM) …
access ports, is a novel non-volatile memory and has potential as scratchpad memory (SPM) …
[PDF][PDF] Design and Code Optimization for Systems with Next-generation Racetrack Memories
AA Khan - 2022 - cfaed.tu-dresden.de
DESIGN AND CODE OPTIMIZATION FOR SYSTEMS WITH NEXT-GENERATION RACETRACK
MEMORIES Dissertation for the purpose of obtaining the d Page 1 DESIGN AND CODE …
MEMORIES Dissertation for the purpose of obtaining the d Page 1 DESIGN AND CODE …