Heterogeneous architectures consisting of general-purpose CPUs and throughput-
optimized GPUs are projected to be the dominant computing platforms for many classes of …

There exists an urgent need for determining the right amount and type of specialization
while making a heterogeneous system as programmable and flexible as possible …

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 …

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 …

In recent years, designing specialized manycore heterogeneous architectures for deep
learning kernels has become an area of great interest. However, the typical on-chip …

As communication energy exceeds computation energy in future technologies, traditional on-
chip electrical interconnects face fundamental challenges in the many-core era. Photonic …

Silicon-photonic link technology promises to satisfy the growing need for high bandwidth,
low-latency and energy-efficient network-on-chip (NoC) architectures. While silicon-photonic …

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 …

Heterogeneous System Architectures (HSA) that integrate cores of different architectures
(CPU, GPU, etc.) on single chip are gaining significance for many class of applications to …