# FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs

arXiv: https://arxiv.org/abs/1807.04093 · HTML: https://mlsanity.com/paper/1807.04093/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml](https://mlsanity.com/paper/2207.00559/) (2022)
- [Accelerating Recurrent Neural Networks for Gravitational Wave Experiments](https://mlsanity.com/paper/2106.14089/) (2021)
- [Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator](https://mlsanity.com/paper/2106.06048/) (2021)
- [Benchmarking Quantized Neural Networks on FPGAs with FINN](https://mlsanity.com/paper/2102.01341/) (2021)
- [MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks](https://mlsanity.com/paper/2108.01192/) (2021)
- [Efficient Non-linear Calculators](https://mlsanity.com/paper/2109.12686/) (2021)
- [NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models](https://mlsanity.com/paper/2403.01845/) (2024)
- [Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model](https://mlsanity.com/paper/2506.19178/) (2025)
- [FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs](https://mlsanity.com/paper/2506.20810/) (2025)
- [Architectural Design and Performance Analysis of FPGA based AI Accelerators: A Comprehensive Review](https://mlsanity.com/paper/2603.08740/) (2026)

## Abstract (arXiv)

It is well known that many types of artificial neural networks, including recurrent networks, can achieve a high classification accuracy even with low-precision weights and activations. The reduction in precision generally yields much more efficient hardware implementations in regards to hardware cost, memory requirements, energy, and achievable throughput. In this paper, we present the first systematic exploration of this design space as a function of precision for Bidirectional Long Short-Term Memory (BiLSTM) neural network. Specifically, we include an in-depth investigation of precision vs. accuracy using a fully hardware-aware training flow, where during training quantization of all aspects of the network including weights, input, output and in-memory cell activations are taken into consideration. In addition, hardware resource cost, power consumption and throughput scalability are explored as a function of precision for FPGA-based implementations of BiLSTM, and multiple approaches of parallelizing the hardware. We provide the first open source HLS library extension of FINN for parameterizable hardware architectures of LSTM layers on FPGAs which offers full precision flexibility and allows for parameterizable performance scaling offering different levels of parallelism within the architecture. Based on this library, we present an FPGA-based accelerator for BiLSTM neural network designed for optical character recognition, along with numerous other experimental proof points for a Zynq UltraScale+ XCZU7EV MPSoC within the given design space.