Joint Circuit-System Design Space Exploration of Multiplier Unit Structure for Energy-Efficient Vector Processors

2015 ◽  
Author(s):  
Ivan Ratkovic ◽  
Oscar Palomar ◽  
Milan Stanic ◽  
Milovan Duric ◽  
Djordje Peic ◽  
...  
Keyword(s):  
System Design ◽  
Energy Efficient ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Unit Structure ◽  
Efficient Vector ◽  
Vector Processors

FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators

2021 ◽  
Vol 29 (4) ◽  
pp. 716-729
Author(s):  
Alberto Marchisio ◽  
Vojtech Mrazek ◽  
Muhammad Abdullah Hanif ◽  
Muhammad Shafique
Keyword(s):  
Energy Efficient ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Low Latency

Design Space Exploration for Quantifying a System Model’s Feasible Domain

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Brad J. Larson ◽  
Christopher A. Mattson
Keyword(s):  
System Design ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
System Model ◽  
Design Decisions ◽  
System Models ◽  
Model Domain ◽  
Compositional System ◽  
Multidisciplinary System

A major challenge in multidisciplinary system design is predicting the effects of design decisions at the point these decisions are being made. Because decisions at the beginning of system design, when the least is known about the new system, have the greatest impact on its final behavior, designers are increasingly interested in using compositional system models (system models created from independent models of system components) to validate design decisions early in and throughout system design. Compositional system models, however, have several failure modes that often result in infeasible or failed model evaluation. In addition, these models change frequently as designs are refined, changing the model domain (set of valid inputs and states). To compute valid results, the system model inputs and states must remain within this domain throughout simulation. This paper develops an algorithm to efficiently quantify the system model domain. To do this, we (1) present a formulation for system model feasibility and identify types of system model failures, (2) develop a design space exploration algorithm that quantifies the system model domain, and (3) illustrate this algorithm using a solar-powered unmanned aerial vehicle model. This algorithm enables systematic improvements of compositional system model feasibility.

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