What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Computer network
Parallel computing, Computer architecture, Embedded system, Dram and Phase-change memory are his primary areas of study.
Cache pollution, Cache coloring, Microarchitecture, Cache and Task parallelism are the core of his Parallel computing study.
His Microarchitecture research includes themes of Dark silicon, Multi-core processor and Speedup.
The study incorporates disciplines such as TRIPS architecture, Software deployment, Microprocessor, Instruction set and Bandwidth in addition to Computer architecture.
His Embedded system study combines topics from a wide range of disciplines, such as Scalability, Distributed memory, Memory hierarchy, Registered memory and Server.
His Dram study integrates concerns from other disciplines, such as Memory architecture, Hardware architecture and Operating system.
His most cited work include:
- The SimpleScalar tool set, version 2.0 (2726 citations)
- Modeling the effect of technology trends on the soft error rate of combinational logic (1328 citations)
- Dark silicon and the end of multicore scaling (1276 citations)
What are the main themes of his work throughout his whole career to date?
Doug Burger focuses on Parallel computing, Computer architecture, Microarchitecture, Embedded system and Compiler.
His study in Multi-core processor, Cache, Speedup, Instruction set and Instruction-level parallelism is carried out as part of his studies in Parallel computing.
His work focuses on many connections between Computer architecture and other disciplines, such as TRIPS architecture, that overlap with his field of interest in Execution model.
His research in Microarchitecture intersects with topics in Microprocessor, Operand and System on a chip.
He combines subjects such as Dram, Computer hardware, Scalability, Memory management and Server with his study of Embedded system.
His Compiler study combines topics in areas such as Software, Dataflow and Heuristics.
He most often published in these fields:
- Parallel computing (33.88%)
- Computer architecture (19.67%)
- Microarchitecture (16.94%)
What were the highlights of his more recent work (between 2014-2020)?
- Cloud computing (4.92%)
- Embedded system (14.75%)
- Field-programmable gate array (3.28%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Cloud computing, Embedded system, Field-programmable gate array, Encoding and Server.
His Cloud computing study is focused on Operating system in general.
His study on Reconfigurable computing is often connected to Flow network as part of broader study in Embedded system.
His Field-programmable gate array research is multidisciplinary, incorporating elements of Computer architecture and Latency.
The Server study combines topics in areas such as Data center, Hyperscale, Scalability and Resilience.
Multi-core processor is closely connected to Network performance in his research, which is encompassed under the umbrella topic of Scalability.
Between 2014 and 2020, his most popular works were:
- A cloud-scale acceleration architecture (307 citations)
- A configurable cloud-scale DNN processor for real-time AI (218 citations)
- Serving DNNs in Real Time at Datacenter Scale with Project Brainwave (160 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Computer network
His primary areas of investigation include Cloud computing, Server, Embedded system, Field-programmable gate array and Hyperscale.
The various areas that he examines in his Server study include Software and Scalability.
His research combines Computer architecture and Field-programmable gate array.
His work deals with themes such as Latency, Compile time, Microservices, System on a chip and SIMD, which intersect with Computer architecture.
His Stratix study incorporates themes from Artificial neural network and Microarchitecture.
Operating system is represented through his Load balancing and Multi-core processor research.
This overview was generated by a machine learning system which analysed the scientist’s
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