This paper addresses the multi-robot planning problem to generate trajectories for maximum information gathering from an area of interest. To solve this problem, the proposed framework first leverages a Gaussian Mixture Model (GMM) as prior knowledge for modeling informative regions in the target area. Taking samples from the GMM, informative robot paths are then computed that optimize the travel costs subject to energy budgets. Decomposing these paths into waypoints, robot trajectories are planned while respecting kinematic constraints and subsequently replanned online to avoid collisions among themselves. The GMM model is incrementally updated by incorporating the gathered information. We demonstrate that our framework can achieve a significant amount of information gain with the optimal travel distance. We also provide a realistic simulation with a team of mobile robots in a port infrastructure monitoring setting.

Demand for high performance deep learning (DL) inference in software applications is growing rapidly. DL workloads run on myriad platforms, including general purpose processors (CPU), system-on-chip (SoC) with accelerators, graphics processing units (GPU), and neural processing unit (NPU) addin cards. DL software engineers typically must choose between relatively slow general hardware (e.g., CPUs, SoCs) or relatively expensive, large, power-hungry hardware (e.g., GPUs, NPUs). This paper describes Centaur Technology's Ncore, the industry's first high-performance DL coprocessor technology integrated into an x86 SoC with server-class CPUs. Ncore's 4096 byte-wide SIMD architecture supports INT8, UINT8, INT16, and BF16 datatypes, with 20 tera-operations-per-second compute capability. Ncore shares the SoC ring bus for low-latency communication and work sharing with eight 64-bit x86 cores, offering flexible support for new and evolving models. The x86 SoC platform can further scale out performance via multiple sockets, systems, or third-party PCIe accelerators. Ncore's software stack automatically converts quantized models for Ncore consumption and leverages existing DL frameworks. In MLPerf's Inference v0.5 closed division benchmarks, Ncore achieves 1218 IPS throughput and 1.05ms latency on ResNet-50v1.5 and achieves lowest latency of all Mobilenet-V1 submissions (329μs). Ncore yields 23x speedup over other x86 vendor percore throughput, while freeing its own x86 cores for other work. Ncore is the only integrated solution among the memory intensive neural machine translation (NMT) submissions.