# FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference

arXiv: https://arxiv.org/abs/1904.02835 · HTML: https://mlsanity.com/paper/1904.02835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

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

- [HQNAS: Auto CNN deployment framework for joint quantization and architecture search](https://mlsanity.com/paper/2210.08485/) (2022)

## Abstract (arXiv)

To improve the throughput and energy efficiency of Deep Neural Networks (DNNs) on customized hardware, lightweight neural networks constrain the weights of DNNs to be a limited combination (denoted as k∈1,2) of powers of 2. In such networks, the multiply-accumulate operation can be replaced with a single shift operation, or two shifts and an add operation. To provide even more design flexibility, the k for each convolutional filter can be optimally chosen instead of being fixed for every filter. In this paper, we formulate the selection of k to be differentiable, and describe model training for determining k-based weights on a per-filter basis. Over 46 FPGA-design experiments involving eight configurations and four data sets reveal that lightweight neural networks with a flexible k value (dubbed FLightNNs) fully utilize the hardware resources on Field Programmable Gate Arrays (FPGAs), our experimental results show that FLightNNs can achieve 2× speedup when compared to lightweight NNs with k=2, with only 0.1% accuracy degradation. Compared to a 4-bit fixed-point quantization, FLightNNs achieve higher accuracy and up to 2× inference speedup, due to their lightweight shift operations. In addition, our experiments also demonstrate that FLightNNs can achieve higher computational energy efficiency for ASIC implementation.