Energy-performance design exploration of a low-power microprogrammed deep-learning accelerator

2018 ◽  
Author(s):  
Giulia Santoro ◽  
Mario R. Casu ◽  
Valentino Peluso ◽  
Andrea Calimera ◽  
Massimo Alioto
Keyword(s):  
Deep Learning ◽  
Low Power ◽  
Energy Performance ◽  
Design Exploration

scholarly journals Architectural Design Exploration Using Generative Design: Framework Development and Case Study of a Residential Block

Buildings ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 201
Author(s):  
Jani Mukkavaara ◽  
Marcus Sandberg
Keyword(s):  
Architectural Design ◽  
Solution Space ◽  
Energy Performance ◽  
Design System ◽  
Future Research ◽  
Generative Design ◽  
Design Exploration ◽  
Novel Approach ◽  
Generative Algorithms

The use of generative design has been suggested to be a novel approach that allows designers to take advantage of computers’ computational capabilities in the exploration of design alternatives. However, the field is still sparsely explored. Therefore, this study aimed to investigate the potential use of generative design in an architectural design context. A framework was iteratively developed alongside a prototype, which was eventually demonstrated in a case study to evaluate its applicability. The development of a residential block in the northern parts of Sweden served as the case. The findings of this study further highlight the potential of generative design and its promise in an architectural context. Compared to previous studies, the presented framework is open to other generative algorithms than mainly genetic algorithms and other evaluation models than, for instance, energy performance models. The paper also presents a general technical view on the functionality of the generative design system, as well as elaborating on how to explore the solution space in a top-down fashion. This paper moves the field of generative design further by presenting a generic framework for architectural design exploration. Future research needs to focus on detailing how generative design should be applied and when in the design process.

scholarly journals Gender Classification System Based on Deep Learning in Low Power Embedded Board

2017 ◽  
Vol 6 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Hyunwook Jeong ◽  
Dae Hoe Kim ◽  
Wisam J. Baddar ◽  
Yong Man Ro
Keyword(s):  
Deep Learning ◽  
Low Power ◽  
Classification System ◽  
Gender Classification
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