What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Central processing unit
Tajana Rosing focuses on Embedded system, System on a chip, Real-time computing, Energy management and Energy consumption.
His Embedded system study incorporates themes from Computer hardware, Reliability, State, Power management and Dynamic priority scheduling.
The concepts of his System on a chip study are interwoven with issues in Multiprocessing, Electronic engineering and UltraSPARC.
The study incorporates disciplines such as Temperature control and Reliability engineering in addition to Multiprocessing.
His Real-time computing research is multidisciplinary, incorporating perspectives in Wind power, Schedule, Data center, Renewable energy and Operations research.
His research combines Overhead and Energy consumption.
His most cited work include:
- PDRAM: a hybrid PRAM and DRAM main memory system (466 citations)
- Energy Harvesting for Structural Health Monitoring Sensor Networks (336 citations)
- Temperature aware task scheduling in MPSoCs (223 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Embedded system, Efficient energy use, Energy consumption, Real-time computing and Parallel computing.
His studies deal with areas such as Workload, Scheduling, Power management and Server as well as Embedded system.
His Efficient energy use research incorporates elements of Floating point, Scalability, Quality of service, Bottleneck and Computation.
His Energy consumption research includes themes of Distributed computing and Energy management.
The various areas that Tajana Rosing examines in his Real-time computing study include Wireless sensor network, Reliability engineering and Renewable energy.
Tajana Rosing combines subjects such as Non-volatile memory, Interleaved memory and Content-addressable memory with his study of Parallel computing.
He most often published in these fields:
- Embedded system (22.22%)
- Efficient energy use (20.49%)
- Energy consumption (15.97%)
What were the highlights of his more recent work (between 2017-2021)?
- Speedup (11.11%)
- Efficient energy use (20.49%)
- Artificial intelligence (10.42%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Speedup, Efficient energy use, Artificial intelligence, Artificial neural network and Parallel computing.
His research in Speedup intersects with topics in Cluster analysis, Inference, Computer engineering and Memory management.
His studies in Efficient energy use integrate themes in fields like Floating point, Scalability, Quality of service, Bottleneck and Computation.
His work is dedicated to discovering how Artificial intelligence, Task are connected with Routing and other disciplines.
He has researched Parallel computing in several fields, including Energy consumption, Non-volatile memory, Content-addressable memory and General-purpose computing on graphics processing units.
The Energy consumption study which covers Latency that intersects with Embedded system.
Between 2017 and 2021, his most popular works were:
- SpotTune: Transfer Learning Through Adaptive Fine-Tuning (68 citations)
- FloatPIM: in-memory acceleration of deep neural network training with high precision (53 citations)
- Approximate Computing Using Multiple-Access Single-Charge Associative Memory (40 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Central processing unit
The scientist’s investigation covers issues in Speedup, Efficient energy use, Artificial intelligence, Parallel computing and Content-addressable memory.
His Speedup research includes elements of Artificial neural network, Computational complexity theory, Inference, Cluster analysis and Memory management.
His Efficient energy use research includes themes of Floating point and Computation.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
His Parallel computing research incorporates elements of Energy consumption, Non-volatile memory, Semiconductor memory and Graph.
His work in Energy consumption addresses issues such as Memory architecture, which are connected to fields such as Memory access pattern.
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