What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Electrical engineering
Rakesh Kumar mainly investigates Embedded system, Multiprocessing, Parallel computing, Multi-core processor and Thread.
His work carried out in the field of Embedded system brings together such families of science as Scalability and CMOS.
His study looks at the relationship between Multiprocessing and fields such as Crossbar switch, as well as how they intersect with chemical problems.
His study looks at the relationship between Parallel computing and topics such as Chip, which overlap with System on a chip, Electrical efficiency and Single-core.
His Multi-core processor study combines topics in areas such as Computer architecture, Reduction and System software.
His study looks at the intersection of Reduction and topics like Energy consumption with Efficient energy use.
His most cited work include:
- Single-ISA heterogeneous multi-core architectures: the potential for processor power reduction (707 citations)
- Single-ISA Heterogeneous Multi-Core Architectures for Multithreaded Workload Performance (519 citations)
- Interconnections in Multi-Core Architectures: Understanding Mechanisms, Overheads and Scaling (389 citations)
What are the main themes of his work throughout his whole career to date?
Rakesh Kumar spends much of his time researching Embedded system, Parallel computing, Artificial intelligence, Multi-core processor and Distributed computing.
His research integrates issues of Voltage, Software, System software and Efficient energy use in his study of Embedded system.
His Parallel computing study incorporates themes from Thread and Chip.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Computer vision.
In his study, Multiprocessing is inextricably linked to Computer architecture, which falls within the broad field of Multi-core processor.
His biological study spans a wide range of topics, including Redundancy, Scalability and Network packet.
He most often published in these fields:
- Embedded system (15.50%)
- Parallel computing (14.50%)
- Artificial intelligence (14.50%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (14.50%)
- Computer vision (11.00%)
- Distributed computing (11.00%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Distributed computing, Machine learning and Image are his primary areas of study.
In general Artificial intelligence, his work in Segmentation, Inference engine and Classification methods is often linked to Spatial analysis linking many areas of study.
His studies deal with areas such as Computation, Computer-aided and Rebar as well as Computer vision.
His Distributed computing research incorporates themes from Linear network coding, Critical infrastructure, Network topology, Redundancy and Mobile device.
Embedded system covers Rakesh Kumar research in Microcontroller.
Within one scientific family, Rakesh Kumar focuses on topics pertaining to Reduction under Reliability, and may sometimes address concerns connected to Computer hardware.
Between 2017 and 2021, his most popular works were:
- Guaranteeing local differential privacy on ultra-low-power systems (12 citations)
- Hardware Acceleration of Graph Neural Networks (11 citations)
- AUGMENTING REALITY USING SEMANTIC SEGMENTATION (9 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Electrical engineering
His scientific interests lie mostly in Distributed computing, Artificial intelligence, Mobile device, Computer vision and Computer hardware.
His Distributed computing study combines topics in areas such as Redundancy, Network topology, Critical infrastructure and Linear network coding.
His work in the fields of Image, Modality and Multi modal data overlaps with other areas such as Point and 3d perception.
His work carried out in the field of Mobile device brings together such families of science as Data processing, Private information retrieval, Thresholding, Differential privacy and Analytics.
His Computer vision research includes themes of Frame and Computation.
The concepts of his Computer hardware study are interwoven with issues in Low voltage and Pipeline.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
