What is he best known for?
The fields of study he is best known for:
- Operating system
- Central processing unit
- Algorithm
The scientist’s investigation covers issues in Field-programmable gate array, Embedded system, Parallel computing, Routing and Algorithm.
The various areas that Jason Cong examines in his Field-programmable gate array study include Computer architecture, Electronic circuit, Application-specific integrated circuit, Lookup table and Logic synthesis.
His work in Embedded system addresses subjects such as Software, which are connected to disciplines such as Scheduling.
His work in Parallel computing addresses issues such as Scalability, which are connected to fields such as Interconnection, Hierarchy, Bottleneck, Process and Computer engineering.
His studies deal with areas such as Mathematical optimization, Steiner tree problem, RC time constant and Elmore delay as well as Routing.
His Algorithm research is multidisciplinary, relying on both Graph, Netlist, Theoretical computer science and Benchmark.
His most cited work include:
- Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks (1212 citations)
- FlowMap: an optimal technology mapping algorithm for delay optimization in lookup-table based FPGA designs (627 citations)
- High-Level Synthesis for FPGAs: From Prototyping to Deployment (527 citations)
What are the main themes of his work throughout his whole career to date?
Jason Cong mostly deals with Field-programmable gate array, Parallel computing, Embedded system, Algorithm and Routing.
His Field-programmable gate array research incorporates elements of Lookup table, Logic synthesis, Computer architecture and Speedup.
His work deals with themes such as Scheduling, Scalability, Interconnection and Electronic circuit, which intersect with Parallel computing.
The various areas that Jason Cong examines in his Embedded system study include Software, Efficient energy use and Multi-core processor.
The Algorithm study combines topics in areas such as Very-large-scale integration and Mathematical optimization, Minification.
Much of his study explores Routing relationship to Steiner tree problem.
He most often published in these fields:
- Field-programmable gate array (34.12%)
- Parallel computing (24.49%)
- Embedded system (19.43%)
What were the highlights of his more recent work (between 2015-2021)?
- Field-programmable gate array (34.12%)
- Embedded system (19.43%)
- Parallel computing (24.49%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Field-programmable gate array, Embedded system, Parallel computing, Speedup and Software.
Particularly relevant to High-level synthesis is his body of work in Field-programmable gate array.
His High-level synthesis research is multidisciplinary, incorporating elements of Correctness and Benchmark.
His Embedded system study deals with Dram intersecting with Cache.
The study incorporates disciplines such as Systolic array, Compiler and Stencil in addition to Parallel computing.
The concepts of his Speedup study are interwoven with issues in Memory hierarchy and Artificial intelligence.
Between 2015 and 2021, his most popular works were:
- Caffeine: towards uniformed representation and acceleration for deep convolutional neural networks (236 citations)
- Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs (197 citations)
- Scaling for edge inference of deep neural networks (155 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Central processing unit
- Programming language
Field-programmable gate array, Embedded system, Parallel computing, Efficient energy use and Computer architecture are his primary areas of study.
His Field-programmable gate array research is multidisciplinary, incorporating perspectives in Latency, Computer engineering, Key, Bottleneck and Computation.
Jason Cong interconnects Automation and Software in the investigation of issues within Embedded system.
His Parallel computing study integrates concerns from other disciplines, such as Scalability and Porting.
His Efficient energy use study combines topics in areas such as Distributed computing, Computational complexity theory, Cluster, Dynamic programming and FPGA prototype.
His Computer architecture research incorporates themes from High-level synthesis and Artificial neural network, Deep learning, Convolutional neural network, Artificial intelligence.
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