As general-purpose processors have hit the power wall and chip fabrication cost escalates
alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing …

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …

Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …

Hyperscale datacenter providers have struggled to balance the growing need for
specialized hardware (efficiency) with the economic benefits of homogeneity …

To meet the computational demands required of deep learning, cloud operators are turning
toward specialized hardware for improved efficiency and performance. Project Brainwave …

The rapid adoption of heterogeneous computing has driven the integration of Field
Programmable Gate Arrays (FPGAs) into cloud datacenters and flexible System-on-Chips …

Reconfigurable architectures have gained popularity in recent years as they allow the
design of energy-efficient accelerators. Fine-grain fabrics (eg FPGAs) have traditionally …

Industry is increasingly turning to reconfigurable architectures like FPGAs and CGRAs for
improved performance and energy efficiency. Unfortunately, adoption of these architectures …

Recently, FPGA-based CNN accelerators have demonstrated superior energy efficiency
compared to high-performance devices like GPGPUs. However, due to the constrained on …

Performance of in-memory key-value store (KVS) continues to be of great importance as
modern KVS goes beyond the traditional object-caching workload and becomes a key …