What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Algorithm
His primary areas of study are Parallel computing, Speedup, Microarchitecture, Multi-core processor and Compiler.
The Parallel computing study combines topics in areas such as Artificial neural network, Unreachable code, Dead code and Computer engineering.
His research integrates issues of Stochastic gradient descent, Gradient descent, Computational learning theory, Artificial intelligence and Machine learning in his study of Speedup.
His research in Microarchitecture intersects with topics in Hardware description language, Quantization and Verilog.
His Multi-core processor research incorporates themes from ARM architecture, Stratix and Memory footprint.
His Compiler study incorporates themes from Redundant code and Programming paradigm, Imperative programming.
His most cited work include:
- Dark silicon and the end of multicore scaling (1276 citations)
- A reconfigurable fabric for accelerating large-scale datacenter services (639 citations)
- Neural Acceleration for General-Purpose Approximate Programs (466 citations)
What are the main themes of his work throughout his whole career to date?
Hadi Esmaeilzadeh mainly focuses on Speedup, Parallel computing, Artificial neural network, Compiler and Microarchitecture.
His Speedup research is multidisciplinary, incorporating elements of Overhead, Computer engineering, Xeon, Hardware acceleration and Operand.
His studies deal with areas such as Computation and Approximate computing as well as Parallel computing.
The study incorporates disciplines such as Distributed computing, Algorithm, Quantization, Inference and Rendering in addition to Artificial neural network.
The concepts of his Compiler study are interwoven with issues in Cache, Memory footprint and Programmer.
His Microarchitecture study combines topics from a wide range of disciplines, such as Domain, Multi-core processor and Encoding.
He most often published in these fields:
- Speedup (33.33%)
- Parallel computing (26.44%)
- Artificial neural network (22.99%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial neural network (22.99%)
- Speedup (33.33%)
- Data mining (8.05%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial neural network, Speedup, Data mining, Cloud computing and Inference.
His Artificial neural network research integrates issues from Compiler, Quantization, Computer engineering, Rendering and Reinforcement learning.
His Speedup research is under the purview of Parallel computing.
Hadi Esmaeilzadeh has researched Parallel computing in several fields, including Operand, Composability and Leverage.
His work deals with themes such as Recommender system, Deep learning and Data science, which intersect with Cloud computing.
His biological study spans a wide range of topics, including Gradient descent and Optimization problem.
Between 2019 and 2021, his most popular works were:
- Shredder: Learning Noise Distributions to Protect Inference Privacy (22 citations)
- Privacy in Deep Learning: A Survey (15 citations)
- Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices (8 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Algorithm
His primary scientific interests are in Cloud computing, Inference, Edge device, Artificial neural network and Compiler.
His work is dedicated to discovering how Cloud computing, Data mining are connected with Set, Gradient descent and Optimization problem and other disciplines.
His research on Inference concerns the broader Artificial intelligence.
His work carried out in the field of Edge device brings together such families of science as Enhanced Data Rates for GSM Evolution, Edge computing and Server.
He combines subjects such as Dynamic programming, Scheduling, Memory footprint and Reinforcement learning with his study of Artificial neural network.
His Compiler research is multidisciplinary, relying on both Heuristics, Combinatorial optimization, Distributed computing and Computer engineering.
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