---
title: "Efficient Processing of Deep Neural Networks: A Tutorial and"
description: "DNNs reach top accuracy in vision, speech, robotics and other AI tasks, but they demand heavy… 162 references, cited by 101 papers here."
authors: ["Vivienne Sze", "Yu-Hsin Chen", "Tien-Ju Yang", "Joel Emer"]
year: 2017
venue: "Proceedings of the IEEE 2017"
source: "https://arxiv.org/abs/1703.09039"
arxiv: "1703.09039"
licence: "arXiv — non-exclusive distribution license (arXiv OAI)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/"
---
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# Efficient Processing of Deep Neural Networks: A Tutorial and Survey

**Authors:** Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang and Joel Emer · **Year:** 2017 · **Venue:** Proceedings of the IEEE 2017 · **Licence:** [arXiv — non-exclusive distribution license (arXiv OAI)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1703.09039) · [PDF](https://arxiv.org/pdf/1703.09039) · [arXiv 1703.09039](https://arxiv.org/abs/1703.09039) · [OpenAlex](https://openalex.org/W2951963285)

**Citations:** 45 citations (OpenAlex, 2026-10-14) · [101](https://md.research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
DNNs reach top accuracy in vision, speech, robotics and other AI tasks, but they demand heavy computation. This makes them expensive to deploy, especially beyond general-purpose GPUs. Sze, Chen, Yang and Emer (MIT and Nvidia) wrote a combined tutorial and survey on running DNNs faster and with less energy without hurting accuracy or raising hardware cost. It is not a single experiment. The article first places deep learning within AI, machine learning and brain-inspired computing, then explains DNN building blocks, popular models, and tools for research and development. Its core sorts efficiency techniques into three groups:

- hardware-only optimizations that keep outputs bit-wise identical;
- near-data processing with mixed-signal circuits and new memory technologies, aimed at costly data movement;
- joint algorithm–hardware co-design that trades a small amount of accuracy for speed and energy.

It ends with the metrics that fair comparisons of DNN hardware should use. The article matters as a structured entry point for both practitioners and researchers. It also offers a shared vocabulary for judging the many accelerator proposals from academia and industry.

## Key points
- Under the paper's definition, a network counts as deep when it has more than three layers, meaning more than one hidden layer. Typical depth runs from about five to over a thousand layers.
- The paper separates two phases. Training finds the weights by reducing a loss, computed with backpropagation. Inference runs the network with those fixed weights. The paper uses image classification as its running example.
- The survey covers co-design methods such as:
  - reduced precision (BinaryConnect, BinaryNet, XNOR-Net, ternary and logarithmic quantization);
  - pruning (Optimal Brain Damage, energy-aware pruning);
  - compact architectures (SqueezeNet, MobileNets, depthwise separable convolutions);
  - low-rank decomposition;
  - knowledge distillation.
- Sparse and compressed accelerators discussed include EIE, SCNN, Cnvlutin and Minerva. Spiking hardware is represented by IBM TrueNorth.
- The authors provide public resources: a DNN energy estimation tool and a benchmarking page for DNN processors, both on eyeriss.mit.edu.

## Interesting ideas
- **Split optimizations by whether they change outputs.** The paper keeps techniques that give bit-wise identical results apart from those that trade accuracy for efficiency. Any efficiency report, for example on NLP model compression, gets clearer when lossless and lossy gains are kept separate.
- **Make the cost you care about explicit.** Energy-aware pruning removes weights according to estimated energy, not parameter count, so it optimizes the real deployment cost instead of a proxy. The same principle applies to latency, memory, or cloud cost when shrinking models.
- **Treat data movement as the main bottleneck.** The authors argue that moving data, more than arithmetic, dominates energy and throughput. Profiling memory traffic first, not FLOPs, is useful for any large data-processing pipeline.
- **Report a full set of metrics.** The paper argues that new designs should be judged on several considerations together, such as accuracy, throughput, energy and hardware cost, never a single headline number. This guards against cherry-picked comparisons in any benchmark the family publishes.
- **Ship tools alongside the survey.** Releasing an energy estimator and a benchmarking page with the paper lowers the entry barrier for newcomers. This is worth copying for our own resources, such as corpora or treebanks.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 243 works · 76,506 citations

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## Cite

Cite as: Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang and Joel Emer. 2017. Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proceedings of the IEEE 2017.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sze2017efficient,
  title = {{Efficient Processing of Deep Neural Networks: A Tutorial and Survey}},
  author = {Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang and Joel Emer},
  booktitle = {Proceedings of the IEEE 2017},
  year = {2017},
  eprint = {1703.09039},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1703.09039}
}
```

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## References

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*Updated 2 November 2026*