Chiplet-based design, which breaks a system into multiple smaller dice (or “chiplets”) and
reassembles them into a new system chip through advanced packaging, has received …

The increased computational capability in heterogeneous manycore architectures facilitates
the concurrent execution of many applications. This requires, among other things, a flexible …

Convolutional Neural Networks (CNNs) have shown a great deal of success in diverse
application domains including computer vision, speech recognition, and natural language …

The rising use of deep learning and other big-data algorithms has led to an increasing
demand for hardware platforms that are computationally powerful, yet energy-efficient. Due …

Heterogeneous manycore architectures are deployed to simultaneously run multiple and
diverse applications. This requires various computing capabilities (CPUs, GPUs, and …

Data transfer overhead between computing cores and memory hierarchy has been a
persistent issue for von Neumann architectures and the problem has only become more …

Graphics Processing Units (GPUs) are widely-used accelerators for data-parallel
applications. In many GPU applications, GPU memory bandwidth bottlenecks performance …

As we integrate data-parallel GPUs with general-purpose CPUs on a single chip, the
enormous cache traffic generated by GPUs will not only exhaust the limited cache capacity …

Graphics Processing Units (GPUs) employ large register files to accommodate all active
threads and accelerate context switching. Unfortunately, register files are a scalability …

With the skyrocketing advances of process technology, the increased need to process huge
amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing …