What is he best known for?
The fields of study he is best known for:
- Operating system
- Electrical engineering
- Artificial intelligence
Saibal Mukhopadhyay focuses on Electronic engineering, CMOS, Leakage, Static random-access memory and Transistor.
Saibal Mukhopadhyay mostly deals with Integrated circuit design in his studies of Electronic engineering.
His biological study spans a wide range of topics, including Electronic circuit and Process variation, Voltage.
His Leakage research incorporates themes from Threshold voltage and Subthreshold conduction.
As part of one scientific family, Saibal Mukhopadhyay deals mainly with the area of Threshold voltage, narrowing it down to issues related to the Semiconductor device modeling, and often Pass transistor logic, Drain-induced barrier lowering and Short-channel effect.
The various areas that Saibal Mukhopadhyay examines in his Transistor study include Optoelectronics, Nanoelectronics, Logic gate and Circuit design.
His most cited work include:
- Leakage current mechanisms and leakage reduction techniques in deep-submicrometer CMOS circuits (1851 citations)
- Modeling of failure probability and statistical design of SRAM array for yield enhancement in nanoscaled CMOS (432 citations)
- Neurocube: a programmable digital neuromorphic architecture with high-density 3D memory (240 citations)
What are the main themes of his work throughout his whole career to date?
Saibal Mukhopadhyay mainly investigates Electronic engineering, CMOS, Electrical engineering, Artificial intelligence and Power.
His Electronic engineering research integrates issues from Voltage regulator, Transistor and Leakage.
Saibal Mukhopadhyay interconnects Threshold voltage and Subthreshold conduction in the investigation of issues within Leakage.
CMOS connects with themes related to Electronic circuit in his study.
His Electrical engineering research is multidisciplinary, incorporating elements of Optoelectronics and Thermoelectric cooling.
His work deals with themes such as Thermal and Multi-core processor, which intersect with Power.
He most often published in these fields:
- Electronic engineering (36.93%)
- CMOS (16.83%)
- Electrical engineering (15.33%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (12.56%)
- Internal medicine (8.79%)
- Deep learning (5.28%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Internal medicine, Deep learning, Artificial neural network and CMOS.
His study in the field of Object detection also crosses realms of Task.
His Internal medicine study integrates concerns from other disciplines, such as Appendage and Cardiology.
His study in CMOS is interdisciplinary in nature, drawing from both Floating point, Energy, Low-dropout regulator and Voltage regulator.
His study explores the link between Energy and topics such as Synaptic weight that cross with problems in Electronic engineering.
Saibal Mukhopadhyay has researched Electrical efficiency in several fields, including Transistor and Electrical engineering.
Between 2019 and 2021, his most popular works were:
- A Digital Low-Dropout Regulator With Autotuned PID Compensator and Dynamic Gain Control for Improved Transient Performance Under Process Variations and Aging (7 citations)
- Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems (6 citations)
- PhICNet: Physics-Incorporated Convolutional Recurrent Neural Networks for Modeling Dynamical Systems. (5 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Electrical engineering
His primary areas of study are Artificial intelligence, Object detection, Recurrent neural network, CMOS and Computer vision.
The study incorporates disciplines such as Machine learning, Dynamical systems theory, Efficient energy use and Pattern recognition in addition to Artificial intelligence.
His Object detection research incorporates elements of Latency, Artificial neural network, Entropy, Hybrid system and Process variation.
His Recurrent neural network study combines topics from a wide range of disciplines, such as Deep learning and State.
His studies deal with areas such as Test vector, Voltage reference, Advanced Encryption Standard and Leakage as well as CMOS.
His Computer vision research is multidisciplinary, relying on both Edge device and Reinforcement learning.
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