partitioning algorithms
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Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1930
Author(s):  
Azwad Tamir ◽  
Milad Salem ◽  
Jie Lin ◽  
Qutaiba Alasad ◽  
Jiann-shiun Yuan

In this study, we developed a complete flow for the design of monolithic 3D ICs. We have taken the register-transfer level netlist of a circuit as the input and synthesized it to construct the gate-level netlist. Next, we partitioned the circuit using custom-made partitioning algorithms and implemented the place and route flow of the entire 3D IC by repurposing 2D electronic design automation tools. We implemented two different partitioning algorithms, namely the min-cut and the analytical quadratic (AQ) algorithms, to assign the cells in different tiers. We applied our flow on three different benchmark circuits and compared the total power dissipation of the 3D designs with their 2D counterparts. We also compared our results with that of similar works and obtained significantly better performance. Our two-tier 3D flow with AQ partitioner obtained 37.69%, 35.06%, and 12.15% power reduction compared to its 2D counterparts on the advanced encryption standard, floating-point unit, and fast Fourier transform benchmark circuits, respectively. Finally, we analyzed the type of circuits that are more applicable for a 3D layout and the impact of increasing the number of tiers of the 3D design on total power dissipation.


2020 ◽  
Vol 125 ◽  
pp. 103708
Author(s):  
Retief Lubbe ◽  
Wen-Jie Xu ◽  
Daniel N. Wilke ◽  
Patrick Pizette ◽  
Nicolin Govender

2020 ◽  
Vol 10 (14) ◽  
pp. 5004
Author(s):  
Lifan Sun ◽  
Haofang Yu ◽  
Zhumu Fu ◽  
Zishu He ◽  
Fazhan Tao

For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed—it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.


2020 ◽  
Vol 13 (10) ◽  
pp. 1261-1274 ◽  
Author(s):  
Wenfei Fan ◽  
Muyang Liu ◽  
Chao Tian ◽  
Ruiqi Xu ◽  
Jingren Zhou

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