Fast Performance Estimation and Design Space Exploration of SSD Using AI Techniques

2020 ◽  
pp. 1-17
Author(s):  
Jangryul Kim ◽  
Soonhoi Ha
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Performance Estimation

scholarly journals A Fast Performance Estimation Framework for System-Level Design Space Exploration

2012 ◽  
Vol 5 ◽  
pp. 44-54 ◽  
Author(s):  
Seiya Shibata ◽  
Yuki Ando ◽  
Shinya Honda ◽  
Hiroyuki Tomiyama ◽  
Hiroaki Takada
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Performance Estimation ◽  
System Level ◽  
System Level Design ◽  
Level Design

scholarly journals Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 520
Author(s):  
Martin Ferianc ◽  
Hongxiang Fan ◽  
Divyansh Manocha ◽  
Hongyu Zhou ◽  
Shuanglong Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Prediction Method ◽  
Gaussian Process Regression ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation

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.

High-Precision Performance Estimation for the Design Space Exploration of Dynamic Dataflow Programs

2018 ◽  
Vol 4 (2) ◽  
pp. 127-140 ◽  
Author(s):  
Magorzata Michalska ◽  
Simone Casale-Brunet ◽  
Endri Bezati ◽  
Marco Mattavelli
Keyword(s):  
High Precision ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Performance Estimation
Sign in / Sign up

Export Citation Format

Share Document