What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Central processing unit
Muhammad Shafique mostly deals with Embedded system, Dark silicon, Software, Encoder and Computer architecture.
His Embedded system research includes elements of Compiler, Efficient energy use, Reliability and Code generation.
His biological study spans a wide range of topics, including Power, Power density, Electronic engineering and Thermal design power.
His research integrates issues of Multithreading, Reliability engineering, Dependability and Propagation of uncertainty in his study of Software.
His Encoder research incorporates themes from Motion estimation, Data compression and Speedup.
His work in Computer architecture tackles topics such as Instruction set which are related to areas like Reconfigurable computing, Scheme and Field-programmable gate array.
His most cited work include:
- Mapping on multi/many-core systems: survey of current and emerging trends (324 citations)
- A low latency generic accuracy configurable adder (171 citations)
- Reliable on-chip systems in the nano-era: lessons learnt and future trends (151 citations)
What are the main themes of his work throughout his whole career to date?
Muhammad Shafique mainly investigates Embedded system, Artificial intelligence, Efficient energy use, Computer engineering and Distributed computing.
His work deals with themes such as Computer architecture, Reliability, Dark silicon, Instruction set and Software, which intersect with Embedded system.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
Muhammad Shafique has researched Efficient energy use in several fields, including Energy consumption, Power management and Hardware acceleration.
His Computer engineering study combines topics in areas such as Adder and Convolutional neural network.
His Distributed computing research is multidisciplinary, relying on both Scalability and Multi-core processor.
He most often published in these fields:
- Embedded system (22.81%)
- Artificial intelligence (15.21%)
- Efficient energy use (14.06%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (15.21%)
- Computer engineering (11.75%)
- Convolutional neural network (5.99%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer engineering, Convolutional neural network, Embedded system and Efficient energy use are his primary areas of study.
His Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
His Computer engineering study also includes
- Quantization that connect with fields like Process,
- Quantization which is related to area like Contextual image classification.
As part of one scientific family, Muhammad Shafique deals mainly with the area of Convolutional neural network, narrowing it down to issues related to the Speedup, and often Reduction and Energy consumption.
His Embedded system research incorporates elements of Automation, Resource and Control reconfiguration.
His research on Efficient energy use also deals with topics like
- Design space exploration and related Dram, Computer architecture, Memory management and Chip,
- Implementation and Latency most often made with reference to Data mapping.
Between 2019 and 2021, his most popular works were:
- An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks (23 citations)
- Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead (23 citations)
- FT-ClipAct: resilience analysis of deep neural networks and improving their fault tolerance using clipped activation (14 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Central processing unit
Muhammad Shafique focuses on Computer engineering, Convolutional neural network, Artificial intelligence, Efficient energy use and Spiking neural network.
The various areas that Muhammad Shafique examines in his Computer engineering study include Edge device, Quantization, Memory management, MNIST database and Speedup.
His studies in Artificial intelligence integrate themes in fields like Motor impairment, Software and Pattern recognition.
The concepts of his Efficient energy use study are interwoven with issues in Computer architecture, Design space exploration, Energy consumption, Hardware acceleration and Reconfigurability.
His study looks at the relationship between Spiking neural network and topics such as Noise, which overlap with Distributed computing.
His study in Distributed computing is interdisciplinary in nature, drawing from both Cross layer and Reliability.
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