An overview of machine learning within embedded and mobile devices–optimizations and applications
Embedded systems technology is undergoing a phase of transformation owing to the novel
advancements in computer architecture and the breakthroughs in machine learning …
advancements in computer architecture and the breakthroughs in machine learning …
Machine learning for design space exploration and optimization of manycore systems
In the emerging data-driven science paradigm, computing systems ranging from IoT and
mobile to manycores and datacenters play distinct roles. These systems need to be …
mobile to manycores and datacenters play distinct roles. These systems need to be …
Autonomous design space exploration of computing systems for sustainability: Opportunities and challenges
Autonomous Design Space Exploration of Computing Systems for Sustainability:
Opportunities and Challenges Page 1 35 2168-2356/19©2019 IEEE Copublished by the …
Opportunities and Challenges Page 1 35 2168-2356/19©2019 IEEE Copublished by the …
MOOS: A multi-objective design space exploration and optimization framework for NoC enabled manycore systems
The growing needs of emerging applications has posed significant challenges for the design
of optimized manycore systems. Network-on-Chip (NoC) enables the integration of a large …
of optimized manycore systems. Network-on-Chip (NoC) enables the integration of a large …
Deep reinforcement learning enabled self-configurable networks-on-chip for high-performance and energy-efficient computing systems
MF Reza - IEEE Access, 2022 - ieeexplore.ieee.org
Network-on-Chips (NoC) has been the superior interconnect fabric for multi/many-core on-
chip systems because of its scalability and parallelism. On-chip network resources can be …
chip systems because of its scalability and parallelism. On-chip network resources can be …
The advances, challenges and future possibilities of millimeter-wave chip-to-chip interconnections for multi-chip systems
With aggressive scaling of device geometries, density of manufacturing faults is expected to
increase. Therefore, yield of complex Multi-Processor Systems-on-Chips (MP-SoCs) will …
increase. Therefore, yield of complex Multi-Processor Systems-on-Chips (MP-SoCs) will …
Edge AI: Systems design and ML for IoT data analytics
With the explosion in Big Data, it is often forgotten that much of the data nowadays is
generated at the edge. Specifically, a major source of data is users' endpoint devices like …
generated at the edge. Specifically, a major source of data is users' endpoint devices like …
Reinforcement learning enabled routing for high-performance networks-on-chip
Network-on-chip (NoC) has been the standard fabric for multi-core architectures. With the
increase in cores in the multi-core architectures, the probability of congestion increases …
increase in cores in the multi-core architectures, the probability of congestion increases …
Machine learning enabled solutions for design and optimization challenges in networks-on-chip based multi/many-core architectures
MF Reza - ACM Journal on Emerging Technologies in Computing …, 2023 - dl.acm.org
Due to the advancement of transistor technology, a single chip processor can now have
hundreds of cores. Network-on-Chip (NoC) has been the superior interconnect fabric for …
hundreds of cores. Network-on-Chip (NoC) has been the superior interconnect fabric for …
Preference-Aware Constrained Multi-Objective Bayesian Optimization
This paper addresses the problem of constrained multi-objective optimization over black-box
objective functions with practitioner-specified preferences over the objectives when a large …
objective functions with practitioner-specified preferences over the objectives when a large …