4 research outputs found
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Fully convolutional U-shaped neural networks have largely been the dominant
approach for pixel-wise image segmentation. In this work, we tackle two defects
that hinder their deployment in real-world applications: 1) Predictions lack
uncertainty quantification that may be crucial to many decision-making systems;
2) Large memory storage and computational consumption demanding extensive
hardware resources. To address these issues and improve their practicality we
demonstrate a few-parameter compact Bayesian convolutional architecture, that
achieves a marginal improvement in accuracy in comparison to related work using
significantly fewer parameters and compute operations. The architecture
combines parameter-efficient operations such as separable convolutions,
bilinear interpolation, multi-scale feature propagation and Bayesian inference
for per-pixel uncertainty quantification through Monte Carlo Dropout. The best
performing configurations required fewer than 2.5 million parameters on diverse
challenging datasets with few observations.Comment: Accepted for publication at ICANN 2021. Code at:
https://github.com/martinferianc/ComBiNe
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation. In this work, we tackle two defects that hinder their deployment in real-world applications: 1) Predictions lack uncertainty quantification that may be crucial to many decision-making systems; 2) Large memory storage and computational consumption demanding extensive hardware resources. To address these issues and improve their practicality we demonstrate a few-parameter compact Bayesian convolutional architecture, that achieves a marginal improvement in accuracy in comparison to related work using significantly fewer parameters and compute operations. The architecture combines parameter-efficient operations such as separable convolutions, bilinear interpolation, multi-scale feature propagation and Bayesian inference for per-pixel uncertainty quantification through Monte Carlo Dropout. The best performing configurations required fewer than 2.5 million parameters on diverse challenging datasets with few observations
Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators
Contemporary advances in neural networks (NNs) have demonstrated their potential in
different applications such as in image classification, object detection or natural language processing.
In particular, reconfigurable accelerators have been widely used for the acceleration of NNs due to
their reconfigurability and efficiency in specific application instances. To determine the configuration
of the accelerator, it is necessary to conduct design space exploration to optimize the performance.
However, the process of design space exploration is time consuming because of the slow performance evaluation for different configurations. Therefore, there is a demand for an accurate and fast
performance prediction method to speed up design space exploration. This work introduces a novel
method for fast and accurate estimation of different metrics that are of importance when performing
design space exploration. The method is based on a Gaussian process regression model parametrised
by the features of the accelerator and the target NN to be accelerated. We evaluate the proposed
method together with other popular machine learning based methods in estimating the latency and
energy consumption of our implemented accelerator on two different hardware platforms targeting
convolutional neural networks. We demonstrate improvements in estimation accuracy, without the
need for significant implementation effort or tuning
Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators
Contemporary advances in neural networks (NNs) have demonstrated their potential in different applications such as in image classification, object detection or natural language processing. In particular, reconfigurable accelerators have been widely used for the acceleration of NNs due to their reconfigurability and efficiency in specific application instances. To determine the configuration of the accelerator, it is necessary to conduct design space exploration to optimize the performance. However, the process of design space exploration is time consuming because of the slow performance evaluation for different configurations. Therefore, there is a demand for an accurate and fast performance prediction method to speed up design space exploration. This work introduces a novel method for fast and accurate estimation of different metrics that are of importance when performing design space exploration. The method is based on a Gaussian process regression model parametrised by the features of the accelerator and the target NN to be accelerated. We evaluate the proposed method together with other popular machine learning based methods in estimating the latency and energy consumption of our implemented accelerator on two different hardware platforms targeting convolutional neural networks. We demonstrate improvements in estimation accuracy, without the need for significant implementation effort or tuning