What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Software
Timothy Sherwood mainly focuses on Real-time computing, Computer engineering, Parallel computing, Embedded system and Distributed computing.
His Real-time computing research includes elements of Pipeline, Data stream mining, Byte, Benchmark and Speedup.
Timothy Sherwood has included themes like Multithreading, Thread, Latency and Pointer in his Parallel computing study.
As a member of one scientific family, Timothy Sherwood mostly works in the field of Multithreading, focusing on Program optimization and, on occasion, Basic block.
His Basic block study incorporates themes from Program analysis and Re-order buffer.
The concepts of his Embedded system study are interwoven with issues in Operand, Software, Resource allocation and Memory protection.
His most cited work include:
- Automatically characterizing large scale program behavior (1491 citations)
- Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications (474 citations)
- Phase tracking and prediction (449 citations)
What are the main themes of his work throughout his whole career to date?
Timothy Sherwood spends much of his time researching Embedded system, Software, Computer hardware, Parallel computing and Real-time computing.
As a part of the same scientific family, Timothy Sherwood mostly works in the field of Embedded system, focusing on Memory protection and, on occasion, Shared memory and Security policy.
His research combines Profiling and Software.
His study looks at the relationship between Computer hardware and topics such as Information flow, which overlap with Hardware description language and Distributed computing.
He has researched Real-time computing in several fields, including Latency and Computer engineering.
His Computer engineering research incorporates elements of Finite-state machine, Byte and Network packet.
He most often published in these fields:
- Embedded system (25.76%)
- Software (14.65%)
- Computer hardware (11.62%)
What were the highlights of his more recent work (between 2016-2021)?
- Computer hardware (11.62%)
- Computer engineering (9.60%)
- Software (14.65%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Computer hardware, Computer engineering, Software, CMOS and Python.
The Static random-access memory and Cycles per instruction research he does as part of his general Computer hardware study is frequently linked to other disciplines of science, such as Leakage, Memory systems and Adder, therefore creating a link between diverse domains of science.
His Computer engineering research includes themes of Elliptic curve cryptography, Computation, Edge device and Cryptographic protocol.
His Software study deals with Code intersecting with Programming language.
His Charm study in the realm of Programming language interacts with subjects such as Memoization.
His study explores the link between Computer architecture and topics such as Hardware compatibility list that cross with problems in Field-programmable gate array.
Between 2016 and 2021, his most popular works were:
- Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints (20 citations)
- A pythonic approach for rapid hardware prototyping and instrumentation (20 citations)
- DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints (13 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Software
His primary areas of study are CMOS, Software, Computer engineering, Network architecture and Uncertainty quantification.
His CMOS study combines topics in areas such as Decision tree, External Data Representation, Theoretical computer science and Frequency domain.
His research in Software intersects with topics in Artificial neural network, Memory address, Edge device and Management science.
His study in Computer engineering is interdisciplinary in nature, drawing from both Cryptographic primitive, Real-time computing, Hardware security module, Advanced Encryption Standard and Public-key cryptography.
His Network architecture research is multidisciplinary, relying on both Memory hierarchy, Relation and Leverage, Artificial intelligence.
His studies in Uncertainty quantification integrate themes in fields like Computing Methodologies, Surrogate model, Multi-core processor and Risk assessment.
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.
