Paper:
Published as a conference paper at ICLR 2018 WRPN: WIDE REDUCED-PRECISION NETWORKS
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, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs. One way to reduce this large memory footprint is to reduce the precision of activations. However, past works have shown that reducing the precision of activations hurts model accuracy. We study schemes to train networks from scratch using reduced-precision activations without hurting accuracy. We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network. As a result, one can significantly improve the execution efficiency (e.g. reduce dynamic memory footprint, memory bandwidth and computational energy) and speed up the training and inference process with appropriate hardware support. We call our scheme WRPN -wide reducedprecision networks. We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.

Review:
This is a well-written paper with good comparisons to a number of earlier approaches. It focuses on an approach to get similar accuracy at lower precision, in addition to cutting down the compute costs. Results with 2-bit activations and 4-bit weights seem to match baseline accuracy across the models listed in the paper.
Originality
This seems to be first paper that consistently matches baseline results below int-8 accuracy, and shows a promising future direction.
Significance
Going down to below 8-bits and potentially all the way down to binary (1-bit weights and activations) is a promising direction for future hardware design. It has the potential to give good results at lower compute and more significantly in providing a lower power option, which is the biggest constraint for higher compute today. 
Pros:
- Positive results with low precision (4-bit, 2-bit and even 1-bit)
- Moving the state of the art in low precision forward
- Strong potential impact, especially on constrained power environments (but not limited to them)
- Uses same hyperparameters as original training, making the process of using this much simpler.
Cons/Questions
- They mention not quantizing the first and last layer of every network. How much does that impact the overall compute? 
- Is there a certain width where 1-bit activation and weights would match the accuracy of the baseline model? This could be interesting for low power case, even if the "effective compute" is larger than the baseline.