# FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

Source: [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](<https://www.sota2.com/research/paper/68fa63071e209131ce9fc7cb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:32:19.733Z

Authors: Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers

Published: 2016-12-01T22:19:47.000Z

## Abstract

Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we present FINN, a framework for building fast and flexible FPGA accelerators using a flexible heterogeneous streaming architecture. By utilizing a novel set of optimizations that enable efficient mapping of binarized neural networks to hardware, we implement fully connected, convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements. On a ZC706 embedded FPGA platform drawing less than 25 W total system power, we demonstrate up to 12.3 million image classifications per second with 0.31 {\\mu}s latency on the MNIST dataset with 95.8% accuracy, and 21906 image classifications per second with 283 {\\mu}s latency on the CIFAR-10 and SVHN datasets with respectively 80.1% and 94.9% accuracy. To the best of our knowledge, ours are the fastest classification rates reported to date on these benchmarks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on MNIST (Accuracy and Hardware Efficiency Metrics)](<https://www.sota2.com/research/sota/image-classification-on-mnist-accuracy-and-hardware-efficiency-metrics.md>): Image Classification; MNIST; Accuracy: 95.8.

- [Image Classification on MNIST (test) (Standard Performance)](<https://www.sota2.com/research/sota/image-classification-on-mnist-test-standard-performance.md>): Image Classification; MNIST (test); Accuracy: 98.86.

- [Image Classification on CIFAR-10 (test) (Accuracy)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test-accuracy.md>): Image Classification; CIFAR-10 (test); Accuracy: 80.1.

- [Image Classification on CIFAR-10 (test)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test.md>): Image Classification; CIFAR-10 (test); Accuracy: 80.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1612.07119>)

[PDF](<https://arxiv.org/pdf/1612.07119.pdf>)