Synthesis of On-Chip Interconnection Structures:From Point-to-Point Links to Networks-on-Chip

Packet-switched networks-on-chip (NOC) have been advocated as the solution to the challenge of organizing efficient and reliable communication structures among the components of a system-on-chip (SOC). A critical issue in designing a NOC is to determine its topology given the set of point-to-point communication requirements among these components. We present a novel approach to on-chip communication synthesis that is based on the iterative combination of two efficient computational steps: (1) an application of the k-Median algorithm to coarsely determine the global communication structure (which may turned out not be a network after all), and a (2) a variation of the shortest-path algorithm in order to finely tune the data flows on the communication channels. The application of our method to case studies taken from the literature shows that we can automatically synthesize optimal NOC topologies for multi-core on-chip processors and it offers new insights on why NOC are not necessarily a value proposition for some classes of applcation-specific SOCs

A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed

MulCh: a Multi-layer Channel Router using One, Two, and Three Layer Partitions

Chameleon, a channel router for three layers of interconnect, has been implemented to accept specification of an arbitrary number of layers. Chameleon is based on a strategy of decomposing the multilayer problem into two- and three-layer problems in which one of the layers is reserved primarily for vertical wire runs and the other layer(s) for horizontal runs. In some situations, however, it is advantageous to consider also layers that allow the routing of entire nets, using both horizontal and vertical wires. MulCh is a multilayer channel router that extends the algorithms of Chameleon in this direction. MulCh can route channels with any number of layers and automatically chooses a good assignment of wiring strategies to the different layers. In test cases, MulCh shows significant improvement over Chameleon in terms of channel width, net length, and number of vias

Digital Sensitivity: Predicting signal interaction using functional analysis

Abstract Maintaining signal integrity in digital systems is becoming increasingly dicult due to the rising number of analog effects seen in deep sub-micron design. One such eect, the signal crosstalk problem, is now a serious design concern. Signals which couple electrically may not aect system behavior because of timing or function in the digital domain. If we can isolate observable coupling eects then we can constrain layout synthesis to eliminate the

Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning

Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest. This work addresses the power-constrained CPP problem with recharge for battery-limited unmanned aerial vehicles (UAVs). In this problem, a notable challenge emerges from integrating recharge journeys into the overall coverage strategy, highlighting the intricate task of making strategic, long-term decisions. We propose a novel proximal policy optimization (PPO)-based deep reinforcement learning (DRL) approach with map-based observations, utilizing action masking and discount factor scheduling to optimize coverage trajectories over the entire mission horizon. We further provide the agent with a position history to handle emergent state loops caused by the recharge capability. Our approach outperforms a baseline heuristic, generalizes to different target zones and maps, with limited generalization to unseen maps. We offer valuable insights into DRL algorithm design for long-horizon problems and provide a publicly available software framework for the CPP problem.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Satisfiability Modulo Theory Approach to Secure State Reconstruction in Differentially Flat Systems Under Sensor Attacks

We address the problem of estimating the state of a differentially flat system from measurements that may be corrupted by an adversarial attack. In cyber-physical systems, malicious attacks can directly compromise the system's sensors or manipulate the communication between sensors and controllers. We consider attacks that only corrupt a subset of sensor measurements. We show that the possibility of reconstructing the state under such attacks is characterized by a suitable generalization of the notion of s-sparse observability, previously introduced by some of the authors in the linear case. We also extend our previous work on the use of Satisfiability Modulo Theory solvers to estimate the state under sensor attacks to the context of differentially flat systems. The effectiveness of our approach is illustrated on the problem of controlling a quadrotor under sensor attacks.Comment: arXiv admin note: text overlap with arXiv:1412.432