# FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks

arXiv: https://arxiv.org/abs/1809.04570 · HTML: https://mlsanity.com/paper/1809.04570/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 30 of 56)

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## Abstract (arXiv)

Convolutional Neural Networks have rapidly become the most successful machine learning algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded computing-systems. While the underlying arithmetic is structurally simple, compute and memory requirements are challenging. One of the promising opportunities is leveraging reduced-precision representations for inputs, activations and model parameters. The resulting scalability in performance, power efficiency and storage footprint provides interesting design compromises in exchange for a small reduction in accuracy. FPGAs are ideal for exploiting low-precision inference engines leveraging custom precisions to achieve the required numerical accuracy for a given application. In this article, we describe the second generation of the FINN framework, an end-to-end tool which enables design space exploration and automates the creation of fully customized inference engines on FPGAs. Given a neural network description, the tool optimizes for given platforms, design targets and a specific precision. We introduce formalizations of resource cost functions and performance predictions, and elaborate on the optimization algorithms. Finally, we evaluate a selection of reduced precision neural networks ranging from CIFAR-10 classifiers to YOLO-based object detection on a range of platforms including PYNQ and AWS,F1, demonstrating new unprecedented measured throughput at 50TOp/s on AWS-F1 and 5TOp/s on embedded devices.