What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Central processing unit
David Brooks spends much of his time researching Embedded system, Microprocessor, Computer architecture, Microarchitecture and Chip.
His Embedded system study combines topics from a wide range of disciplines, such as Wireless, Wireless sensor network, Frequency scaling, Voltage and Key distribution in wireless sensor networks.
The Microprocessor study combines topics in areas such as Reliability engineering and CPU core voltage.
His work carried out in the field of Computer architecture brings together such families of science as Compiler, Cache coloring, Adaptation and Implementation.
David Brooks has included themes like Power analysis, Field, Floorplan, Operand and MMX in his Compiler study.
His work deals with themes such as Multithreading, CPU cache, Parallel computing, Power management and Electronic engineering, which intersect with Chip.
His most cited work include:
- Wattch: a framework for architectural-level power analysis and optimizations (2571 citations)
- Dynamic thermal management for high-performance microprocessors (784 citations)
- System level analysis of fast, per-core DVFS using on-chip switching regulators (612 citations)
What are the main themes of his work throughout his whole career to date?
David Brooks focuses on Embedded system, Artificial intelligence, Electronic engineering, Voltage and Computer architecture.
His study in the field of Microprocessor also crosses realms of Clock gating.
In his study, Reduction is strongly linked to Real-time computing, which falls under the umbrella field of Microprocessor.
His studies deal with areas such as Machine learning and Software as well as Artificial intelligence.
His Electronic engineering research integrates issues from Boost converter, Forward converter and Voltage regulator.
His studies in Computer architecture integrate themes in fields like Hardware acceleration, Compiler and Microarchitecture.
He most often published in these fields:
- Embedded system (19.10%)
- Artificial intelligence (13.89%)
- Electronic engineering (12.50%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (13.89%)
- Deep learning (9.72%)
- Inference (10.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Deep learning, Inference, Artificial neural network and Speedup.
His research integrates issues of Scalability, Hardware acceleration, Software, CUDA and Machine learning in his study of Artificial intelligence.
David Brooks has researched Deep learning in several fields, including Computer architecture, Recommender system, Systems design, Scheduling and Cloud computing.
His Computer architecture study typically links adjacent topics like Macro.
His Inference research includes elements of Variety, Computer engineering and Bayesian inference.
The concepts of his Speedup study are interwoven with issues in Microarchitecture and Memory bandwidth.
Between 2018 and 2021, his most popular works were:
- MLPerf Training Benchmark. (68 citations)
- The Architectural Implications of Facebook's DNN-Based Personalized Recommendation (56 citations)
- Benchmarking TPU, GPU, and CPU Platforms for Deep Learning (53 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 Artificial intelligence, Deep learning, Inference, Software and Distributed computing.
His Deep learning research is multidisciplinary, relying on both Computer architecture, Artificial neural network, Hardware acceleration, Systems design and Speedup.
His Computer architecture research incorporates elements of CUDA and Efficient energy use.
His work focuses on many connections between Speedup and other disciplines, such as Microarchitecture, that overlap with his field of interest in Bottleneck and System deployment.
His Inference research is multidisciplinary, incorporating elements of Recommender system, Overhead, Reduction and Cluster analysis.
His Software study combines topics in areas such as Scalability, Software engineering and Benchmark.
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