Optical on-chip data transmission enabled by silicon photonics (SiP) is widely considered a
key technology to overcome the bandwidth and energy limitations of electrical interconnects …

A Survey of Silicon Photonics for Energy-Efficient Manycore Computing Page 1 60 2168-2356/20©2020
IEEE Copublished by the IEEE CEDA, IEEE CASS, IEEE SSCS, and TTTC IEEE Design&Test …

In pursuit of higher inference accuracy, deep neural network (DNN) models have
significantly increased in complexity and size. To overcome the consequent computational …

Numerous challenges present themselves when scaling traditional on-chip electrical
networks to large manycore processors. Some of these challenges include high latency …

Chiplet-based convolution neural network (CNN) accelerators have emerged as a promising
solution to provide substantial processing power and on-chip memory capacity for CNN …

The complexity and size of recent deep neural network (DNN) models have increased
significantly in pursuit of high inference accuracy. Chiplet-based accelerator is considered a …

Chiplet-based architectures have been proposed to scale computing systems for deep
neural networks (DNNs). Prior work has shown that for the chiplet-based DNN accelerators …

While many new applications prefer manycore processor with a large number of cores, the
exploding communications among multiple cores, caches, and off-chip memories is posing a …

In shared environments like cloud-based datacenters, hardware accelerators are deployed
to meet the scale-out computation demands of deep neural network (DNN) inference tasks …

Cache coherence overhead in manycore systems is becoming prominent with the increase
of system scale. However, traditional electrical networks restrict the efficiency of cache …