182 citations · 237 across the 7 of their papers we have counts for
7 papers
RAD-Sim: Rapid Architecture Exploration for Novel Reconfigurable Acceleration Devices
Andrew Boutros, Eriko Nurvitadhi, Vaughn Betz
With the continued growth in field-programmable gate array (FPGA) capacity and their incorporation into new environments such as datacenters, we have witnessed the introduction of…
FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems
Rui Ma, Evangelos Georganas, Alexander Heinecke +2
Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-in…
Automatic Compiler Based FPGA Accelerator for CNN Training
Shreyas Kolala Venkataramanaiah, Yufei Ma, Shihui Yin +4
Training of convolutional neural networks (CNNs)on embedded platforms to support on-device learning is earning vital importance in recent days. Designing flexible training hard-war…
Exploration of Low Numeric Precision Deep Learning Inference Using Intel FPGAs
Philip Colangelo, Nasibeh Nasiri, Asit Mishra +3
CNNs have been shown to maintain reasonable classification accuracy when quantized to lower precisions. Quantizing to sub 8-bit activations and weights can result in accuracy falli…
WRPN: Wide Reduced-Precision Networks
Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook +1
For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps,…
WRPN: Training and Inference using Wide Reduced-Precision Networks
Asit Mishra, Jeffrey J Cook, Eriko Nurvitadhi +1
For computer vision applications, prior works have shown the efficacy of reducing the numeric precision of model parameters (network weights) in deep neural networks but also that…
Accelerating Deep Convolutional Networks using low-precision and sparsity
Ganesh Venkatesh, Eriko Nurvitadhi, Debbie Marr
We explore techniques to significantly improve the compute efficiency and performance of Deep Convolution Networks without impacting their accuracy. To improve the compute efficien…