# A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks

arXiv: https://arxiv.org/abs/2101.09693 · HTML: https://mlsanity.com/paper/2101.09693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems](https://mlsanity.com/paper/1511.06931/) (2015)
- [Dialog-based Language Learning](https://mlsanity.com/paper/1604.06045/) (2016)
- [Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off](https://mlsanity.com/paper/1607.05418/) (2016)
- [Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks](https://mlsanity.com/paper/1810.00518/) (2018)
- [Gated End-to-End Memory Networks](https://mlsanity.com/paper/1610.04211/) (2016)
- [Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA](https://mlsanity.com/paper/1805.07978/) (2018)

## Built on by (most cited first, 1 of 1)

- [Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications](https://mlsanity.com/paper/2204.11786/) (2022)

## Abstract (arXiv)

In this work, to limit the number of required attention inference hops in memory-augmented neural networks, we propose an online adaptive approach called A2P-MANN. By exploiting a small neural network classifier, an adequate number of attention inference hops for the input query is determined. The technique results in elimination of a large number of unnecessary computations in extracting the correct answer. In addition, to further lower computations in A2P-MANN, we suggest pruning weights of the final FC (fully-connected) layers. To this end, two pruning approaches, one with negligible accuracy loss and the other with controllable loss on the final accuracy, are developed. The efficacy of the technique is assessed by using the twenty question-answering (QA) tasks of bAbI dataset. The analytical assessment reveals, on average, more than 42% fewer computations compared to the baseline MANN at the cost of less than 1% accuracy loss. In addition, when used along with the previously published zero-skipping technique, a computation count reduction of up to 68% is achieved. Finally, when the proposed approach (without zero-skipping) is implemented on the CPU and GPU platforms, up to 43% runtime reduction is achieved.