# ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1706.02393 · HTML: https://mlsanity.com/paper/1706.02393/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 13 of 13)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [S3: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks](https://mlsanity.com/paper/2107.03453/) (2021)
- [Compacting Deep Neural Networks for Internet of Things: Methods and Applications](https://mlsanity.com/paper/2103.11083/) (2021)
- [PECAN: A Product-Quantized Content Addressable Memory Network](https://mlsanity.com/paper/2208.13571/) (2022)
- [DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization](https://mlsanity.com/paper/2208.09708/) (2022)
- [TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential](https://mlsanity.com/paper/2304.11631/) (2023)
- [MWQ: Multiscale Wavelet Quantized Neural Networks](https://mlsanity.com/paper/2103.05363/) (2021)
- [MinUn: Accurate ML Inference on Microcontrollers](https://mlsanity.com/paper/2210.16556/) (2022)
- [Low-bit Shift Network for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2207.07497/) (2022)
- [Ultra-low Precision Multiplication-free Training for Deep Neural Networks](https://mlsanity.com/paper/2302.14458/) (2023)
- [Accelerating PoT Quantization on Edge Devices](https://mlsanity.com/paper/2409.20403/) (2024)
- [PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs](https://mlsanity.com/paper/2605.06082/) (2026)
- [HaShiFlex: A High-Throughput Hardened Shifter DNN Accelerator with Fine-Tuning Flexibility](https://mlsanity.com/paper/2512.12847/) (2025)

## Abstract (arXiv)

In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN is based on a power-of-two weight representation and, as a result, performs only shift and addition operations. Furthermore, ShiftCNN substantially reduces computational cost of convolutional layers by precomputing convolution terms. Such an optimization can be applied to any CNN architecture with a relatively small codebook of weights and allows to decrease the number of product operations by at least two orders of magnitude. The proposed architecture targets custom inference accelerators and can be realized on FPGAs or ASICs. Extensive evaluation on ImageNet shows that the state-of-the-art CNNs can be converted without retraining into ShiftCNN with less than 1% drop in accuracy when the proposed quantization algorithm is employed. RTL simulations, targeting modern FPGAs, show that power consumption of convolutional layers is reduced by a factor of 4 compared to conventional 8-bit fixed-point architectures.