Optimal die placement for interposer-based 3D ICs

Osmolovskyi, Sergii; Knechtel, Johann; Markov, Igor L.; Lienig, Jens

doi:10.1109/aspdac.2018.8297375

Cited by 15 publications

(2 citation statements)

References 14 publications

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“…The floorplan affects the overall area, the routing wirelength, the thermal effects, and the reliability of the design. Prior arts [20], [21] only consider the area and wirelength of the floorplan, while ignoring the performance metrics, such as the data movement frequency and communication latency between different chiplets. If the optimization of the floorplan can take the performance metrics into consideration, the final performance of the chiplet system can be improved.…”

Section: Floorplan Of Chipletsmentioning

confidence: 99%

Scene-aware Learning Network for Radar Object Detection

Zheng

Yue

Keutzer

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in self-driving. However, cameras tend to fail in bad driving conditions, e.g. bad weather or weak lighting, while LiDAR scanners are too expensive to get widely deployed in commercial applications. Radar has been drawing more and more attention due to its robustness and low cost. In this paper, we propose a scene-aware radar learning framework for accurate and robust object detection. First, the learning framework contains branches conditioning on the scene category of the radar sequence; with each branch optimized for a specific type of scene. Second, three different 3D autoencoder-based architectures are proposed for radar object detection and ensemble learning is performed over the different architectures to further boost the final performance. Third, we propose novel scene-aware sequence mix augmentation (SceneMix) and scene-specific post-processing to generate more robust detection results. In the ROD2021 Challenge, we achieved a final result of average precision of 75.0% and an average recall of 81.0%. Moreover, in the parking lot scene, our framework ranks first with an average precision of 97.8% and an average recall of 98.6%, which demonstrates the effectiveness of our framework. CCS CONCEPTS• Computing methodologies → Object detection; Scene understanding; Neural networks.

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Section: Floorplan Of Chipletsmentioning

confidence: 99%

Scene-aware Learning Network for Radar Object Detection

Zheng

Yue

Keutzer

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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“…In the physical layer, Funke et al [14] have proposed various algorithms that exhaustively search for optimal placement and routing solutions for up to six chiplets. The recent work of Osmonolovskyi et al [24] handles up to 11-chiplet design complexity using pruning methods. Minz et al [22] and Fang et al [13] focus on routing of inter-chiplet links on an interposer.…”

Section: Related Workmentioning

confidence: 99%

A cross-layer methodology for design and optimization of networks in 2.5D systems

Coskun

Eris

Joshi

et al. 2018

Proceedings of the International Conference on Computer-Aided Design

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2.5D integration technology is gaining popularity in the design of homogeneous and heterogeneous many-core computing systems. 2.5D network design, both inter-and intra-chiplet, impacts overall system performance as well as its manufacturing cost and thermal feasibility. This paper introduces a cross-layer methodology for designing networks in 2.5D systems. We optimize the network design and chiplet placement jointly across logical, physical, and circuit layers to achieve an energy-efficient network, while maximizing system performance, minimizing manufacturing cost, and adhering to thermal constraints. In the logical layer, our cooptimization considers eight different network topologies. In the physical layer, we consider routing, microbump assignment, and microbump pitch constraints to account for the extra costs associated with microbump utilization in the inter-chiplet communication. In the circuit layer, we consider both passive and active links with five different link types, including a gas station link design. Using our cross-layer methodology results in more accurate determination of (superior) inter-chiplet network and 2.5D system designs compared to prior methods. Compared to 2D systems, our approach achieves 29% better performance with the same manufacturing cost, or 25% lower cost with the same performance.

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