What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Computer network
Viktor K. Prasanna focuses on Field-programmable gate array, Parallel computing, Distributed computing, Embedded system and Floating point.
His Field-programmable gate array research incorporates themes from Logic synthesis, Regular expression and Kernel.
His work deals with themes such as Scalability and Matrix multiplication, which intersect with Parallel computing.
His Distributed computing study incorporates themes from Wireless sensor network, Scheduling, Graph and Resource allocation.
His biological study deals with issues like Intrusion detection system, which deal with fields such as Pattern matching.
The concepts of his Floating point study are interwoven with issues in Computer hardware, Pipeline, Hardware acceleration, Multiplication and Speedup.
His most cited work include:
- Fast Regular Expression Matching Using FPGAs (581 citations)
- Energy-latency tradeoffs for data gathering in wireless sensor networks (338 citations)
- Heterogeneous computing: challenges and opportunities (269 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Parallel computing, Field-programmable gate array, Distributed computing, Scalability and Embedded system.
In his articles, Viktor K. Prasanna combines various disciplines, including Parallel computing and Throughput.
His Field-programmable gate array study combines topics from a wide range of disciplines, such as Floating point, Computer architecture, Efficient energy use and Matrix multiplication.
The various areas that Viktor K. Prasanna examines in his Distributed computing study include Wireless sensor network, Computer network, Scheduling and Heuristics.
His work in Wireless sensor network covers topics such as Real-time computing which are related to areas like Smart grid.
Particularly relevant to Network packet is his body of work in Computer network.
He most often published in these fields:
- Parallel computing (33.74%)
- Field-programmable gate array (21.48%)
- Distributed computing (15.05%)
What were the highlights of his more recent work (between 2014-2021)?
- Parallel computing (33.74%)
- Field-programmable gate array (21.48%)
- Smart grid (6.43%)
In recent papers he was focusing on the following fields of study:
Viktor K. Prasanna mostly deals with Parallel computing, Field-programmable gate array, Smart grid, Speedup and Scalability.
The study incorporates disciplines such as Auxiliary memory, Shortest path problem and Graph in addition to Parallel computing.
Viktor K. Prasanna integrates many fields in his works, including Field-programmable gate array and Throughput.
His Smart grid study combines topics in areas such as Distributed computing, Grid, Microgrid, Real-time computing and Demand response.
His research integrates issues of Computer network, Cloud computing, Analytics and Big data in his study of Distributed computing.
His Speedup research is multidisciplinary, incorporating perspectives in Parallel algorithm, Bipartite graph and Cache.
Between 2014 and 2021, his most popular works were:
- Frequency Domain Acceleration of Convolutional Neural Networks on CPU-FPGA Shared Memory System (95 citations)
- GraphSAINT: Graph Sampling Based Inductive Learning Method (82 citations)
- A Framework for Generating High Throughput CNN Implementations on FPGAs (49 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
Viktor K. Prasanna mainly investigates Parallel computing, Field-programmable gate array, Smart grid, Distributed computing and Speedup.
His research in Parallel computing intersects with topics in Sorting network, Auxiliary memory and Shortest path problem.
Viktor K. Prasanna performs multidisciplinary studies into Field-programmable gate array and Throughput in his work.
The Smart grid study combines topics in areas such as Data modeling, Grid, Real-time computing, Information privacy and Demand response.
Viktor K. Prasanna combines subjects such as Scalability, Analytics, Data stream mining, Cloud computing and Big data with his study of Distributed computing.
Viktor K. Prasanna interconnects CPU cache, Cache, Xeon, Multi-core processor and Partition in the investigation of issues within Speedup.
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.
