FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
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.
Code (3)
Tasks
General ClassificationSimilar Papers 제목 키워드 기반
Scaling Binarized Neural Networks on Reconfigurable Logic
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suited to reconfigurable logic devices, whic…
General ClassificationFast Binarized Neural Network Training with Partial Pre-training
Binarized neural networks, networks with weights and activations constrained to lie in a 2-element set, allow for more time- and resource-efficient inference than standard floating-point networks. However, binarized neur…
daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices
It is always well believed that Binary Neural Networks (BNNs) could drastically accelerate the inference efficiency by replacing the arithmetic operations in float-valued Deep Neural Networks (DNNs) with bit-wise operati…
A Sugeno Integral View of Binarized Neural Network Inference
In this article, we establish a precise connection between binarized neural networks (BNNs) and Sugeno integrals. The advantage of the Sugeno integral is that it provides a framework for representing the importance of in…
MOBIUS: Model-Oblivious Binarized Neural Networks
A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., oblivious neural network or encrypted pred…
BIG-bench Machine LearningmodelPredictionPrivacy Preserving