What is he best known for?
The fields of study he is best known for:
- Operating system
- Central processing unit
- Algorithm
His primary scientific interests are in Embedded system, Non-volatile memory, Computer hardware, Static random-access memory and Field-programmable gate array.
His study in the field of High-level synthesis is also linked to topics like Automation.
His Non-volatile memory research is multidisciplinary, incorporating perspectives in Block, Overhead, Tag RAM and Cache.
His studies deal with areas such as Dram, CPU cache and Memory architecture as well as Static random-access memory.
His Field-programmable gate array research integrates issues from Computer architecture, Field, Flexibility, Design flow and Machine translation.
His Magnetoresistive random-access memory research is multidisciplinary, relying on both Page cache and Write buffer.
His most cited work include:
- Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks (1212 citations)
- A novel architecture of the 3D stacked MRAM L2 cache for CMPs (392 citations)
- Circuit and microarchitecture evaluation of 3D stacking magnetic RAM (MRAM) as a universal memory replacement (285 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Embedded system, Cache, Computer hardware, Parallel computing and Static random-access memory.
His Embedded system study combines topics from a wide range of disciplines, such as Dram, Non-volatile memory and Overhead.
His research is interdisciplinary, bridging the disciplines of Efficient energy use and Cache.
In the subject of general Parallel computing, his work in Cache-only memory architecture and Instruction set is often linked to Register file and Locality, thereby combining diverse domains of study.
His research integrates issues of Block, Magnetoresistive random-access memory, Tag RAM and Backup in his study of Static random-access memory.
His Cache pollution study deals with Cache coloring intersecting with Page cache.
He most often published in these fields:
- Embedded system (30.15%)
- Cache (23.53%)
- Computer hardware (19.12%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (12.50%)
- Convolutional neural network (8.09%)
- Edge computing (5.88%)
In recent papers he was focusing on the following fields of study:
Guangyu Sun spends much of his time researching Artificial intelligence, Convolutional neural network, Edge computing, Deep learning and Inference.
His Convolutional neural network research includes elements of Convolution, Computation, Overhead and Pruning.
Guangyu Sun interconnects Bayesian probability and Computational science in the investigation of issues within Deep learning.
His biological study deals with issues like Computer architecture, which deal with fields such as Field-programmable gate array.
He works mostly in the field of Field-programmable gate array, limiting it down to concerns involving Xeon and, occasionally, Computer engineering.
His Non-volatile memory research includes themes of Random access memory, Quantization, Embedded system and SIMD.
Between 2018 and 2021, his most popular works were:
- Caffeine: Toward Uniformed Representation and Acceleration for Deep Convolutional Neural Networks (87 citations)
- GraphH: A Processing-in-Memory Architecture for Large-Scale Graph Processing (47 citations)
- Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems (35 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Central processing unit
- Algorithm
The scientist’s investigation covers issues in Deep learning, Artificial intelligence, Convolutional neural network, Generalization and Bayesian probability.
His research in Deep learning intersects with topics in Computation, Dynamic network analysis and Reinforcement learning.
His studies deal with areas such as Process, Pruning, Inference, Computational science and Efficient energy use as well as Convolutional neural network.
His Generalization research encompasses a variety of disciplines, including Bounded function, Regularization, Generative grammar, Information leakage and Theoretical computer science.
Guangyu Sun has included themes like Langevin dynamics, Sample, Training set and Markov chain Monte Carlo in his Machine learning study.
His Overfitting research incorporates elements of Contextual image classification, Noise and Differential privacy.
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