# Low- and Mixed-Precision Inference Accelerators

arXiv: https://arxiv.org/abs/2206.12358 · HTML: https://mlsanity.com/paper/2206.12358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [XNOR-Net++: Improved Binary Neural Networks](https://mlsanity.com/paper/1909.13863/) (2019)
- [Estimating or Propagating Gradients Through Stochastic Neurons](https://mlsanity.com/paper/1305.2982/) (2013)
- [XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference](https://mlsanity.com/paper/1807.03010/) (2018)
- [CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency](https://mlsanity.com/paper/2011.01713/) (2020)
- [ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator](https://mlsanity.com/paper/2005.07137/) (2020)
- [Exploring Neural Networks Quantization via Layer-Wise Quantization Analysis](https://mlsanity.com/paper/2012.08420/) (2020)
- [How Flexible is Your Computing System](https://mlsanity.com/paper/2106.01139/) (2021)

## Abstract (arXiv)

With the surging popularity of edge computing, the need to efficiently perform neural network inference on battery-constrained IoT devices has greatly increased. While algorithmic developments enable neural networks to solve increasingly more complex tasks, the deployment of these networks on edge devices can be problematic due to the stringent energy, latency, and memory requirements. One way to alleviate these requirements is by heavily quantizing the neural network, i.e. lowering the precision of the operands. By taking quantization to the extreme, e.g. by using binary values, new opportunities arise to increase the energy efficiency. Several hardware accelerators exploiting the opportunities of low-precision inference have been created, all aiming at enabling neural network inference at the edge. In this chapter, design choices and their implications on the flexibility and energy efficiency of several accelerators supporting extremely quantized networks are reviewed.