What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Algorithm
- Central processing unit
The scientist’s investigation covers issues in Artificial intelligence, Efficient energy use, Energy consumption, Throughput and Artificial neural network.
Her Artificial intelligence study combines topics from a wide range of disciplines, such as Computational complexity theory, Machine learning and Key.
Her research on Efficient energy use also deals with topics like
- Dataflow together with Dram, Chip and Embedded system,
- Convolutional neural network that intertwine with fields like Filter and Computation.
The Energy consumption study combines topics in areas such as Video decoder, Speedup and Integrated circuit.
In her research, Vivienne Sze performs multidisciplinary study on Throughput and Parallel processing.
Her study explores the link between Artificial neural network and topics such as Computer engineering that cross with problems in Backpropagation and Pruning.
Her most cited work include:
- Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks (1393 citations)
- Efficient Processing of Deep Neural Networks: A Tutorial and Survey (1191 citations)
- Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks (670 citations)
What are the main themes of her work throughout her whole career to date?
Vivienne Sze mainly focuses on Artificial intelligence, Energy consumption, Algorithm, Computer vision and Efficient energy use.
Her study on Deep learning and Robotics is often connected to Throughput and Wearable technology as part of broader study in Artificial intelligence.
Vivienne Sze has included themes like Computer hardware, Artificial neural network, Histogram of oriented gradients, Feature extraction and Convolutional neural network in her Energy consumption study.
Her Computer hardware study combines topics in areas such as Video decoder, Decoding methods, Embedded system and Chip.
The Entropy encoding and Bitstream research Vivienne Sze does as part of her general Algorithm study is frequently linked to other disciplines of science, such as Diagonal, therefore creating a link between diverse domains of science.
Her study in Efficient energy use is interdisciplinary in nature, drawing from both Dram, Distributed computing, Computer engineering, Computational complexity theory and Dataflow.
She most often published in these fields:
- Artificial intelligence (33.11%)
- Energy consumption (20.95%)
- Algorithm (18.92%)
What were the highlights of her more recent work (between 2017-2021)?
- Artificial intelligence (33.11%)
- Artificial neural network (11.49%)
- Efficient energy use (17.57%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Artificial intelligence, Artificial neural network, Efficient energy use, Energy consumption and Computer vision.
Her research on Artificial intelligence often connects related topics like Machine learning.
Her studies deal with areas such as Symmetric multiprocessor system, Distributed computing and Computer engineering as well as Efficient energy use.
Her Computer engineering research includes themes of Computation and Code.
Her biological study spans a wide range of topics, including Contextual image classification, Computer hardware, Central processing unit and Optical neural network.
The study incorporates disciplines such as Saccade and Eye movement in addition to Computer vision.
Between 2017 and 2021, her most popular works were:
- NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications (208 citations)
- Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices (180 citations)
- DeeperLab: Single-Shot Image Parser (74 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Central processing unit
Vivienne Sze focuses on Artificial neural network, Efficient energy use, Artificial intelligence, Throughput and Deep learning.
Her Artificial neural network research integrates issues from Contextual image classification, Real-time computing, Inference and Speedup.
Her Efficient energy use study often links to related topics such as Computer engineering.
Her work on Robotics, Search and rescue and Mutual information is typically connected to Robot control and Space exploration as part of general Artificial intelligence study, connecting several disciplines of science.
Her Deep learning research is multidisciplinary, incorporating perspectives in Segmentation and Key.
Vivienne Sze interconnects Computer hardware, Application-specific integrated circuit and Chip in the investigation of issues within Augmented reality.
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