What is he best known for?
The fields of study he is best known for:
- Operating system
- Central processing unit
- Computer network
Youtao Zhang mostly deals with Embedded system, Computer hardware, Phase-change memory, Scalability and Memory architecture.
The study incorporates disciplines such as Schedule, Scheduling and Chip in addition to Embedded system.
His Computer hardware study frequently draws parallels with other fields, such as Overhead.
His studies examine the connections between Overhead and genetics, as well as such issues in Electronic engineering, with regards to Cache.
His research in Scalability intersects with topics in Network planning and design, Latency, Error detection and correction and Access time.
His Non-volatile memory study combines topics in areas such as Memory refresh, Semiconductor memory, Computer memory, Universal memory and Non-volatile random-access memory.
His most cited work include:
- A durable and energy efficient main memory using phase change memory technology (793 citations)
- Phase-Change Technology and the Future of Main Memory (337 citations)
- Energy reduction for STT-RAM using early write termination (293 citations)
What are the main themes of his work throughout his whole career to date?
Youtao Zhang mainly focuses on Embedded system, Computer hardware, Parallel computing, Dram and Computer network.
His Memory architecture study in the realm of Embedded system connects with subjects such as Register file.
Many of his research projects under Computer hardware are closely connected to Phase-change memory with Phase-change memory, tying the diverse disciplines of science together.
Youtao Zhang has included themes like Memory refresh, Non-volatile random-access memory and Semiconductor memory in his Non-volatile memory study.
His studies in Parallel computing integrate themes in fields like Scheduling, Data structure and Data compression.
His work focuses on many connections between Dram and other disciplines, such as Energy consumption, that overlap with his field of interest in Computer engineering.
He most often published in these fields:
- Embedded system (24.42%)
- Computer hardware (18.60%)
- Parallel computing (18.60%)
What were the highlights of his more recent work (between 2018-2021)?
- Embedded system (24.42%)
- Latency (11.05%)
- Dram (18.02%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Embedded system, Latency, Dram, Random access memory and Computer hardware.
His Embedded system research incorporates elements of Server and Memory bandwidth.
The concepts of his Latency study are interwoven with issues in Context switch, Queueing theory and Implementation.
His research integrates issues of Energy consumption, Overhead, Bitwise operation and Memory protection in his study of Dram.
His Random access memory research is multidisciplinary, incorporating perspectives in Parallel computing, Cache, Non-volatile memory, Static random-access memory and Garbage collection.
His Computer hardware research includes themes of Scalability, Interleaving, Row and Resistive random-access memory.
Between 2018 and 2021, his most popular works were:
- A Framework for Memory Oversubscription Management in Graphics Processing Units (26 citations)
- Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks (16 citations)
- ELP2IM: Efficient and Low Power Bitwise Operation Processing in DRAM (10 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Central processing unit
- Computer network
His scientific interests lie mostly in Embedded system, Artificial intelligence, Parallel computing, Artificial neural network and Dram.
His research in Embedded system is mostly concerned with Flash memory.
Server, Encryption, Deep learning and Upload is closely connected to Pattern recognition in his research, which is encompassed under the umbrella topic of Artificial intelligence.
In Parallel computing, he works on issues like Random access memory, which are connected to Exploit and Latency.
His Artificial neural network research is multidisciplinary, relying on both Feature extraction, CMOS, Resistive random-access memory and Crossbar switch.
Youtao Zhang has researched Dram in several fields, including Energy consumption, Logic gate, Charge sharing, Decoding methods and Electrical efficiency.
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.
