Placement
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Related Articles from SNS
Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution
Announce Type: new Abstract: Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems. Despite recent advancements in machine learning for spatial coordinate determination, the temporal dimension of placement sequencing remains largely governed by static heuristics. In this work, we demonstrate that the placement sequence is not merely a preprocessing step but a...
AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placement
arXiv:2604.10432v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) policies have emerged as a versatile paradigm for generalist robotic manipulation. However, precise object placement under compositional language remains challenging for end-to-end VLA policies. Slot-level placement requires reliable slot grounding and centimeter-level geometric precision.
Physics-Guided Geometric Diffusion for Macro Placement Generation
arXiv:2605.16451v2 Announce Type: replace Abstract: Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies and to balance topological connectivity with physical constraints. To bridge this gap, we propose MacroDiff+, a physics-guided geometric diffusion framework.
Compliance-Based Sensor Placement for Force Sensing on a Sensorized Prostate Phantom
arXiv:2606.06977v1 Announce Type: new Abstract: This work presents a compliance-based sensor placement method for force sensing on a sensorized prostate phantom designed for Digital Rectal Examination training. The phantom combines three internal pneumatic chambers, used as intrinsic pressure sensors, with ten surface displacement markers. A finite-element simulation dataset is generated by applying external forces at sampled surface locations, from which a compliance matrix relating force...
Where to Put Safety? Control Barrier Function Placement in Networked Control Systems
arXiv:2603.29792v2 Announce Type: replace Abstract: Control barrier functions (CBFs) are widely used to enforce safety in autonomous systems, yet their placement within networked control architectures remains largely unexplored. In this work, we investigate where to enforce safety in a networked control system in which a remote model predictive controller (MPC) communicates with the plant over a delayed network. We compare two safety strategies: i) a local myopic CBF filter applied at the...
Manchester University to offer work placements to all undergraduates
The University of Manchester is set to offer work placements to all undergraduate students, regardless of their field of study. This initiative aims to provide students, from chemical engineering to classics, with meaningful real-world experience to better prepare them for the job market. This move is noted as a potential first for a large Russell Group university.
Manchester University to offer work placements to all undergraduates
The University of Manchester is set to offer work placements to all undergraduate students across all disciplines. This initiative, which is reportedly a first for a large Russell Group university, aims to provide students with meaningful, real-world experience to better prepare them for the job market.
How Can Reinforcement Learning Achieve Expert-level Placement?
Announce Type: replace Abstract: Chip placement is a critical step in physical design. While reinforcement learning (RL)-based methods have recently emerged, their training primarily focuses on wirelength optimization, and therefore often fail to achieve expert-quality layouts. We identify the reward design as the primary cause for the performance gap with experts, and instead of formalizing intricate processes, we circumvent this by directly learning from expert layouts to derive a reward...
FlowPlace: Flow Matching for Chip Placement
arXiv:2604.23658v2 Announce Type: replace Abstract: Chip placement plays an important role in physical design. While generative models like diffusion models offer promising learning-based solutions, current methods have the following limitations: they use random synthetic data for pre-training, require long sampling times, and often result in overlaps due to their dependence on gradient-based solvers during the sampling process.
EES-CND: Collaborative Neural Decision-Making for Drift-Aware Fault-Tolerant Edge-Cloud Service Placement
arXiv:2606.02259v1 Announce Type: new Abstract: The edge-cloud paradigm improves service delivery by orchestrating resources across edge nodes and cloud data centres. These environments consist of heterogeneous, interconnected computing nodes that cooperate to deliver continuous services. However, their scale and complexity increase vulnerability to failures from hardware malfunctions, software defects, and dynamic operating conditions.