182 citations · 410 across the 8 of their papers we have counts for
8 papers
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs
Anup Sarma, Sonali Singh, Huaipan Jiang +5
Machine/deep-learning (ML/DL) based techniques are emerging as a driving force behind many cutting-edge technologies, achieving high accuracy on computer vision workloads such as i…
Accelerating Sparse Deep Neural Networks
Asit Mishra, Jorge Albericio Latorre, Jeff Pool +5
As neural network model sizes have dramatically increased, so has the interest in various techniques to reduce their parameter counts and accelerate their execution. An active area…
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 & Apprentice: Methods for Training and Inference using Low-Precision Numerics
Asit Mishra, Debbie Marr
Today's high performance deep learning architectures involve large models with numerous parameters. Low precision numerics has emerged as a popular technique to reduce both the com…
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
Asit Mishra, Debbie Marr
Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typic…
Low Precision RNNs: Quantizing RNNs Without Losing Accuracy
Supriya Kapur, Asit Mishra, Debbie Marr
Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization. Quantizing bit-widths of weights and activations results in ru…
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…