What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Central processing unit
The scientist’s investigation covers issues in Embedded system, Computer architecture, Static random-access memory, Electronic engineering and Computer hardware.
His Embedded system research is multidisciplinary, relying on both CPU cache, Cache, Magnetoresistive random-access memory, Interconnection and Cache-only memory architecture.
His biological study deals with issues like Microarchitecture, which deal with fields such as Microprocessor.
He interconnects Parallel computing, Non-volatile memory, eDRAM, Universal memory and CAS latency in the investigation of issues within Static random-access memory.
His work deals with themes such as Negative-bias temperature instability, Resistive random-access memory and Reliability, which intersect with Electronic engineering.
His Computer hardware study combines topics in areas such as Soft error, Energy harvesting and Leakage.
His most cited work include:
- NVSim: A Circuit-Level Performance, Energy, and Area Model for Emerging Nonvolatile Memory (756 citations)
- PRIME: a novel processing-in-memory architecture for neural network computation in ReRAM-based main memory (688 citations)
- A novel architecture of the 3D stacked MRAM L2 cache for CMPs (392 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Embedded system, Electronic engineering, Artificial neural network, Parallel computing and Artificial intelligence.
His Embedded system study combines topics from a wide range of disciplines, such as Dram, Cache, Chip, Non-volatile memory and Static random-access memory.
His Non-volatile memory research is multidisciplinary, incorporating perspectives in Random access memory, Semiconductor memory and Resistive random-access memory.
The various areas that Yuan Xie examines in his Static random-access memory study include Magnetoresistive random-access memory and Tag RAM.
His research investigates the connection between Electronic engineering and topics such as Electronic circuit that intersect with problems in Soft error.
His studies deal with areas such as Computer engineering, Convolutional neural network, Speedup and Computation as well as Artificial neural network.
He most often published in these fields:
- Embedded system (26.83%)
- Electronic engineering (21.79%)
- Artificial neural network (13.17%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial neural network (13.17%)
- Artificial intelligence (11.71%)
- Speedup (8.62%)
In recent papers he was focusing on the following fields of study:
Yuan Xie mainly focuses on Artificial neural network, Artificial intelligence, Speedup, Machine learning and Convolutional neural network.
His Artificial neural network study integrates concerns from other disciplines, such as Software, Computation, Inference and Memristor.
The concepts of his Software study are interwoven with issues in Program optimization, Computer hardware, Theoretical computer science and Cache.
His Memristor study necessitates a more in-depth grasp of Electronic engineering.
His research integrates issues of Computer vision and Pattern recognition in his study of Artificial intelligence.
Yuan Xie combines subjects such as Computer architecture, CUDA and Memory bandwidth with his study of Speedup.
Between 2019 and 2021, his most popular works were:
- Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey (70 citations)
- HyGCN: A GCN Accelerator with Hybrid Architecture (33 citations)
- A Survey of Accelerator Architectures for Deep Neural Networks (30 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Central processing unit
Artificial neural network, Speedup, Artificial intelligence, Parallel computing and Scalability are his primary areas of study.
His research in Artificial neural network intersects with topics in Software framework, Electronic engineering, Convolutional neural network and Computer engineering.
He has included themes like Computer architecture, Bottleneck and Memory bandwidth in his Speedup study.
His study in Parallel computing is interdisciplinary in nature, drawing from both Overhead, Inference and Column.
His studies in Scalability integrate themes in fields like Memristor, Chip, Network topology, Integrated circuit and Central processing unit.
He has researched Pipeline in several fields, including CPU cache, Embedded system, Clock rate, Vector processor and Multi-core processor.
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.
