A Clock Mesh is a structure commonly used in System-on-a-Chip (SoC) designs running at very high clock rates, typically beyond 2.0 GHz, to distribute a clock signal across a large region of a chip. The clock mesh typically is implemented using wires on two adjacent and orthogonal levels of metal in a chip, in which the wires are tied together at each point of intersection. Taps of the mesh are made as needed to service the clocking needs of logic below the mesh. A typical tap would normally service the clocking needs of an area that is in the order of 0.01 sq mm. Several taps are placed beneath the mesh to ensure coverage of the entire area of the block. In some SOC Designs, two or more synchronous clocks are needed to support logic which is placed below the clock mesh. Typically, the synchronous clocks are divided clocks. To meet the timing closure requirements of the logic, when logic of one synchronous clock drives logic associated with the other synchronous clock, it is necessary to provide precise alignment of the clocks, including precise alignment of coincident edges of the synchronous clocks. In this paper, we propose a method of supporting the requirement of multiple synchronous clocks with a single mesh and meeting the Design for Test needs of such designs.

Can knowing where you are assist in perceiving objects in your surroundings, especially under adverse weather and lighting conditions? In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map or pixel-level map-query correspondences. We contribute an algorithm which refines an initial set of candidate object detections and produces a refined subset of highly accurate detections using a prior map. We begin by using visual place recognition (VPR) to retrieve a prior map image for a given query image, then use a binary classification neural network that compares the query and prior map image regions to validate the query detection. Once our classification network is trained, on approximately 1000 query-map image pairs, it is able to improve the performance of vehicle detection when combined with an existing off-the-shelf vehicle detector. We demonstrate our approach using standard datasets across two cities (Oxford and Zurich) under different settings of train-test separation of map-query traverse pairs. We further emphasize the performance gains of our approach against alternative design choices and show that VPR suffices for the task, eliminating the need for precise ground truth localization.

Most 6-DoF localization and SLAM systems use static landmarks but ignore dynamic objects because they cannot be usefully incorporated into a typical pipeline. Where dynamic objects have been incorporated, typical approaches have attempted relatively sophisticated identification and localization of these objects, limiting their robustness or general utility. In this research, we propose a middle ground, demonstrated in the context of autonomous vehicles, using dynamic vehicles to provide limited pose constraint information in a 6-DoF frame-by-frame PnP-RANSAC localization pipeline. We refine initial pose estimates with a motion model and propose a method for calculating the predicted quality of future pose estimates, triggered by whether or not the autonomous vehicle's motion is constrained by the relative frame-to-frame location of dynamic vehicles in the environment. Our approach detects and identifies suitable dynamic vehicles to define these pose constraints to modify a pose filter, resulting in improved recall across a range of localization tolerances from 0.25m to 5m, compared to a state-of-the-art baseline single image PnP method and its vanilla pose filtering. Our constraint detection system is active for approximately 35% of the time on the Ford AV dataset and localization is particularly improved when the constraint detection is active.

Uncertainty pervades through the modern robotic autonomy stack, with nearly every component (e.g., sensors, detection, classification, tracking, behavior prediction) producing continuous or discrete probabilistic distributions. Trajectory forecasting, in particular, is surrounded by uncertainty as its inputs are produced by (noisy) upstream perception and its outputs are predictions that are often probabilistic for use in downstream planning. However, most trajectory forecasting methods do not account for upstream uncertainty, instead taking only the most-likely values. As a result, perceptual uncer-tainties are not propagated through forecasting and predictions are frequently overconfident. To address this, we present a novel method for incorporating perceptual state uncertainty in trajectory forecasting, a key component of which is a new statistical distance-based loss function which encourages predicting uncertainties that better match upstream perception. We evaluate our approach both in illustrative simulations and on large-scale, real-world data, demonstrating its efficacy in propagating perceptual state uncertainty through prediction and producing more calibrated predictions.