# Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design

arXiv: https://arxiv.org/abs/1709.07776 · HTML: https://mlsanity.com/paper/1709.07776/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [Low-Precision Stochastic Gradient Langevin Dynamics](https://mlsanity.com/paper/2206.09909/) (2022)
- [Adaptive Block Floating-Point for Analog Deep Learning Hardware](https://mlsanity.com/paper/2205.06287/) (2022)
- [Number Systems for Deep Neural Network Architectures: A Survey](https://mlsanity.com/paper/2307.05035/) (2023)
- [Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision](https://mlsanity.com/paper/2102.13565/) (2021)
- [Approximating Continuous Convolutions for Deep Network Compression](https://mlsanity.com/paper/2210.08951/) (2022)
- [Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization](https://mlsanity.com/paper/2302.05865/) (2023)
- [TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs](https://mlsanity.com/paper/2606.11357/) (2026)

## Abstract (arXiv)

The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms. As CNN is attributed to the strong endurance to computation errors, employing block floating point (BFP) arithmetics in CNN accelerators could save the hardware cost and data traffics efficiently, while maintaining the classification accuracy. In this paper, we verify the effects of word width definitions in BFP to the CNN performance without retraining. Several typical CNN models, including VGG16, ResNet-18, ResNet-50 and GoogLeNet, were tested in this paper. Experiments revealed that 8-bit mantissa, including sign bit, in BFP representation merely induced less than 0.3% accuracy loss. In addition, we investigate the computational errors in theory and develop the noise-to-signal ratio (NSR) upper bound, which provides the promising guidance for BFP based CNN engine design.