101 citations · 204 across the 3 of their papers we have counts for
3 papers
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration
Fabrizio Ottati, Chang Gao, Qinyu Chen +4
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense…
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi +19
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vi…
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Yifan Yang, Qijing Huang, Bichen Wu +8
Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a res…