What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Algorithm
Dharmendra S. Modha mostly deals with TrueNorth, Parallel computing, Neuromorphic engineering, Cluster analysis and Synapse.
His TrueNorth research incorporates themes from Frame, Pixel, Chip, Von Neumann architecture and Cognitive computing.
His study in the field of Cache also crosses realms of Throughput.
His Neuromorphic engineering study incorporates themes from Scalability, Computer hardware, Static random-access memory and Spiking neural network.
His Cluster analysis study integrates concerns from other disciplines, such as Disjoint sets, Algorithm, Set and Pattern recognition.
The concepts of his Synapse study are interwoven with issues in Theoretical computer science, Neuron, Dendrite and Topology.
His most cited work include:
- A million spiking-neuron integrated circuit with a scalable communication network and interface (2022 citations)
- Concept Decompositions for Large Sparse Text Data Using Clustering (1133 citations)
- Information-theoretic co-clustering (939 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computer hardware, Artificial neural network, Artificial intelligence, Neuromorphic engineering and Electronic circuit.
Dharmendra S. Modha combines subjects such as Set, Electronic engineering, Core and Spiking neural network with his study of Computer hardware.
His studies in Artificial neural network integrate themes in fields like Substrate, Inference, Neuron and Parallel computing.
His Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition.
His research in Neuromorphic engineering is mostly focused on TrueNorth.
His research in TrueNorth focuses on subjects like Scalability, which are connected to Computer architecture.
He most often published in these fields:
- Computer hardware (22.67%)
- Artificial neural network (22.22%)
- Artificial intelligence (16.44%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial neural network (22.22%)
- Artificial intelligence (16.44%)
- Computer hardware (22.67%)
In recent papers he was focusing on the following fields of study:
Dharmendra S. Modha mainly focuses on Artificial neural network, Artificial intelligence, Computer hardware, Algorithm and TrueNorth.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Substrate, Inference, Neuron, Parallel computing and Topology.
His Artificial intelligence research integrates issues from Sequence, Computer vision and Pattern recognition.
His research integrates issues of Routing, Hardware software, Multi-core processor and Asynchronous communication in his study of Computer hardware.
His Quantization and Error detection and correction study in the realm of Algorithm connects with subjects such as Sequence and Quantization.
His study on TrueNorth is covered under Neuromorphic engineering.
Between 2015 and 2020, his most popular works were:
- Convolutional networks for fast, energy-efficient neuromorphic computing (424 citations)
- A Low Power, Fully Event-Based Gesture Recognition System (151 citations)
- Deep neural networks are robust to weight binarization and other non-linear distortions (65 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Algorithm
His scientific interests lie mostly in TrueNorth, Artificial intelligence, Algorithm, Quantization and Neuromorphic engineering.
Dharmendra S. Modha has researched TrueNorth in several fields, including Feature extraction, Computer architecture and Massively parallel, Parallel computing.
His Parallel computing study integrates concerns from other disciplines, such as Telecommunications network, Backpropagation and Efficient energy use.
He has included themes like Latency and Computer vision in his Artificial intelligence study.
His Algorithm research includes elements of Dimension and Robustness.
The study incorporates disciplines such as Boltzmann machine, Very-large-scale integration, Markov process and Electronic design automation in addition to Neuromorphic engineering.
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