What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Central processing unit
His primary areas of study are Embedded system, Electronic circuit, Electronic engineering, Software and Efficient energy use.
He studies Embedded system, focusing on System on a chip in particular.
His work carried out in the field of Electronic circuit brings together such families of science as Logic synthesis, Power optimization and Algorithm.
The Electronic engineering study combines topics in areas such as Non-volatile memory and Integrated circuit.
His Software study integrates concerns from other disciplines, such as System testing, Computer engineering and Fault coverage.
Anand Raghunathan interconnects Algorithm design, Key, Static random-access memory and Cache in the investigation of issues within Efficient energy use.
His most cited work include:
- Security in embedded systems: Design challenges (404 citations)
- Low-Power Digital Signal Processing Using Approximate Adders (403 citations)
- A study of the energy consumption characteristics of cryptographic algorithms and security protocols (340 citations)
What are the main themes of his work throughout his whole career to date?
Anand Raghunathan focuses on Embedded system, Electronic engineering, Software, Efficient energy use and Parallel computing.
Speedup is closely connected to Instruction set in his research, which is encompassed under the umbrella topic of Embedded system.
His Electronic engineering research includes themes of Power management, Electronic circuit and Reduction.
His studies deal with areas such as Logic synthesis, Logic gate and Algorithm as well as Electronic circuit.
His research investigates the connection with Efficient energy use and areas like Energy consumption which intersect with concerns in Computer engineering and High-level synthesis.
Parallel computing is frequently linked to Scheduling in his study.
He most often published in these fields:
- Embedded system (25.22%)
- Electronic engineering (14.47%)
- Software (13.38%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial neural network (6.80%)
- Artificial intelligence (6.36%)
- Computer engineering (10.53%)
In recent papers he was focusing on the following fields of study:
Anand Raghunathan mainly investigates Artificial neural network, Artificial intelligence, Computer engineering, Efficient energy use and Parallel computing.
His Artificial neural network study combines topics from a wide range of disciplines, such as Energy consumption, Computation, Xeon and Crossbar switch.
His Computer engineering study incorporates themes from Electronic circuit, Key and SIMD.
His Efficient energy use research includes elements of NOR logic, Electronic engineering, CMOS and Data transmission.
His studies in Parallel computing integrate themes in fields like Random access memory, Reduction and Memory controller.
As a part of the same scientific study, Anand Raghunathan usually deals with the Wearable computer, concentrating on State and frequently concerns with Embedded system.
Between 2015 and 2021, his most popular works were:
- ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks (128 citations)
- Spin-Transfer Torque Devices for Logic and Memory: Prospects and Perspectives (128 citations)
- Computing in Memory With Spin-Transfer Torque Magnetic RAM (103 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Central processing unit
Anand Raghunathan mainly focuses on Artificial neural network, Computer engineering, Efficient energy use, Artificial intelligence and Electronic engineering.
The concepts of his Computer engineering study are interwoven with issues in Time delay neural network, Types of artificial neural networks, Multiplication and Approximation algorithm.
His research integrates issues of Natural computing, Embedded system and Unconventional computing in his study of Approximation algorithm.
His Efficient energy use research is multidisciplinary, relying on both Energy consumption and Artificial neuron.
His research in Electronic engineering intersects with topics in Electronic circuit and System on a chip.
His work on Register-transfer level as part of general Electronic circuit study is frequently connected to Kernel, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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