Add to ORKGGenerally, the present disclosure is directed to improving hardware simulation through machine learning. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict hardware test results based on data extracted from hardware
<div>Learning about Systems Using Machine Learning:Towards More Data-Driven Feedback Loops<br></div>...
Simulation-based functional verification is a commonly used technique for hardware verification, wit...
Recent years have seen a dramatic increase in the use of hardware accelerators to perform machine le...
CPUs and dedicated accelerators (namely GPUs and FPGAs) continue to grow increasingly large and comp...
Machine learning applications are computationally expensive, but they can benefit from hardware acce...
Machine learning algorithms are complex to model on hardware. This is due to the fact that these alg...
Knowledge acquisition for an understanding of discrete event simulation systems is a difficult task....
Hardware security is currently a very influential domain, where each year countless works are publis...
Machine learning has consistently proved to be useful in many applications. An integral facet allowi...
The aim of this project is to develop customizable hardware that can perform Machine Learning tasks....
This thesis aims at investigating the capability and feasibility of Machine Learning algorithms for ...
Processor simulators rely on detailed timing models of the processor pipeline to evaluate performanc...
The current thesis investigates data-driven simulation decision-making with field-quality consumer d...
While developing a hardware design, especially programmable hardware, it has proven useful to detect...
Reinforcement learning techniques are provided that generate initial training data to refine a machi...
<div>Learning about Systems Using Machine Learning:Towards More Data-Driven Feedback Loops<br></div>...
Simulation-based functional verification is a commonly used technique for hardware verification, wit...
Recent years have seen a dramatic increase in the use of hardware accelerators to perform machine le...
CPUs and dedicated accelerators (namely GPUs and FPGAs) continue to grow increasingly large and comp...
Machine learning applications are computationally expensive, but they can benefit from hardware acce...
Machine learning algorithms are complex to model on hardware. This is due to the fact that these alg...
Knowledge acquisition for an understanding of discrete event simulation systems is a difficult task....
Hardware security is currently a very influential domain, where each year countless works are publis...
Machine learning has consistently proved to be useful in many applications. An integral facet allowi...
The aim of this project is to develop customizable hardware that can perform Machine Learning tasks....
This thesis aims at investigating the capability and feasibility of Machine Learning algorithms for ...
Processor simulators rely on detailed timing models of the processor pipeline to evaluate performanc...
The current thesis investigates data-driven simulation decision-making with field-quality consumer d...
While developing a hardware design, especially programmable hardware, it has proven useful to detect...
Reinforcement learning techniques are provided that generate initial training data to refine a machi...
<div>Learning about Systems Using Machine Learning:Towards More Data-Driven Feedback Loops<br></div>...
Simulation-based functional verification is a commonly used technique for hardware verification, wit...
Recent years have seen a dramatic increase in the use of hardware accelerators to perform machine le...