The rapid development of deep neural networks (DNNs) is inherently accompanied by the problem of high computational costs. To tackle this challenge, dynamic voltage frequency scaling (DVFS) is emerging as a promising technology for balancing the latency and energy consumption of DNN inference by adjusting the computing frequency of processors. However, most existing models of DNN inference time are based on the CPU-DVFS technique, and directly applying the CPU-DVFS model to DNN inference on GPUs will lead to significant errors in optimizing latency and energy consumption. In this paper, we propose a DVFS-aware latency model to precisely characterize DNN inference time on GPUs. We first formulate the DNN inference time based on extensive experiment results for different devices and analyze the impact of fitting parameters. Then by dividing DNNs into multiple blocks and obtaining the actual inference time, the proposed model is further verified. Finally, we compare our proposed model with the CPU-DVFS model in two specific cases. Evaluation results demonstrate that local inference optimization with our proposed model achieves a reduction of no less than 66% and 69% in inference time and energy consumption respectively. In addition, cooperative inference with our proposed model can improve the partition policy and reduce the energy consumption compared to the CPU-DVFS model.

In the rapidly evolving landscape of autonomous mobile robots, the emphasis on seamless human-robot interactions has shifted towards autonomous decision-making. This paper delves into the intricate challenges associated with robotic autonomy, focusing on navigation in dynamic environments shared with humans. It introduces an embedded real-time tracking pipeline, integrated into a navigation planning framework for effective person tracking and avoidance, adapting a state-of-the-art 2D LiDAR-based human detection network and an efficient multi-object tracker. By addressing the key components of detection, tracking, and planning separately, the proposed approach highlights the modularity and transferability of each component to other applications. Our tracking approach is validated on a quadruped robot equipped with 270{\deg} 2D-LiDAR against motion capture system data, with the preferred configuration achieving an average MOTA of 85.45% in three newly recorded datasets, while reliably running in real-time at 20 Hz on the NVIDIA Jetson Xavier NX embedded GPU-accelerated platform. Furthermore, the integrated tracking and avoidance system is evaluated in real-world navigation experiments, demonstrating how accurate person tracking benefits the planner in optimizing the generated trajectories, enhancing its collision avoidance capabilities. This paper contributes to safer human-robot cohabitation, blending recent advances in human detection with responsive planning to navigate shared spaces effectively and securely.

The use of Autonomous Surface Vehicles, equipped with water quality sensors and artificial vision systems, allows for a smart and adaptive deployment in water resources environmental monitoring. This paper presents a real implementation of a vehicle prototype that to address the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. Furthermore, by means of a stereo-camera, it also can detect and locate macro-plastics in real environments by means of deep visual models, such as YOLOv5. In this paper, experimental results, carried out in Lago Mayor (Sevilla), has been presented as proof of the capabilities of the proposed architecture. The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.

In its third round of submissions, MLCommons released results for MLPerf Inference v1.0. MLPerf is a set of standard AI inference benchmarking tests using seven different applications. These seven tests include a range of workloads that include computer vision, medical imaging, recommender systems, speech recognition, and natural language processing. MLPerf benchmarking measures how fast a trained neural network can process data for each application and its form factor. The results allow unbiased comparison between systems.

AAEON, a specialist in artificial intelligence (AI) edge solutions, has released the BOXER-8251AI AI edge box PC, powered by NVIDIA Jetson Xavier NX. The BOXER-8251AI is said to offer greater performance and is more compact. The device is powered by the Jetson Xavier NX from NVIDIA. Featuring a six-core 64-bit ARM processor, it boasts 384 CUDA cores, 48 Tensor Cores, and two NVDLA engines capable of running multiple neural networks in parallel, delivering accelerated computing performance up to 21 TOPS. Built to bring dedicated AI processing to the edge, the system also features 8GB of LPDDR4 memory and 16GB of onboard eMMC memory that's expandable through the Micro-SD card slot.

Nvidia launched the Jetson Xavier NX embedded System-on-Module (SoM) at the end of last year. It is pin-compatible with the Jetson Nano SoM and includes a CPU, a GPU, PMICs, DRAM, and flash storage. However, it was missing an important accessory, its own development kit. Since a SoM is an embedded board with just a row of connector pins, it is hard to use out-of-the-box. A development board connects all the pins on the module to ports like HDMI, Ethernet, and USB. A Jetson module combined with a development board looks similar to a Raspberry Pi or other Single Board Computers (SBC).

Claimed to be the smallest, most powerful artificial intelligence (AI) supercomputer, the Jetson Xavier NX has been introduced by Nvidia. Althought smaller than a credit card, the Jetson Xavier NX is designed for robotic and embedded computing devices at the edge. The Jetson Xavier NX module is built around a new low-power version of the Xavier SoC and delivers up to 21TOPS at 15W and 14TOPS at 10W. These parameters make it suitable for AI workloads suitable for small commercial robots, drones and IoT systems in industry, for example high-resolution sensors, optical inspection as well as network video recorders, portable medical devices and other IoT systems. The Jetson Xavier NX is based on the Nvidia Volta graphics processor unit (GPU) with 384 Nvidia CUDA cores and 48 Tensor cores and two NVDLA.

NVIDIA today introduced Jetson Xavier NX, the world's smallest, most powerful AI supercomputer for robotic and embedded computing devices at the edge. With a compact form factor smaller than the size of a credit card, the energy-efficient Jetson Xavier NX module delivers server-class performance up to 21 TOPS for running modern AI workloads, and consumes as little as 10 watts of power. Jetson Xavier NX opens the door for embedded edge computing devices that demand increased performance but are constrained by size, weight, power budgets or cost. These include small commercial robots, drones, intelligent high-resolution sensors for factory logistics and production lines, optical inspection, network video recorders, portable medical devices and other industrial IoT systems. "AI has become the enabling technology for modern robotics and embedded devices that will transform industries," said Deepu Talla, vice president and general manager of Edge Computing at NVIDIA.

Nvidia has announced a series of new benchmarks tracking the performance of tools for running AI inference both at the edge and in the datacentre. The results of the MLPerf Inference 0.5, are the industry's first independent suite of AI benchmarks for inference and help to demonstrate the performance of NVIDIA Turing GPUs for datacentres and NVIDIA Xavier system-on-a-chip for edge computing. Nvidia posted the fastest results on new benchmarks measuring the performance of AI inference workloads in datacentres and at the edge -- building on the company's position in recent benchmarks measuring AI training. 'AI is at a tipping point as it moves swiftly from research to large-scale deployment for real applications,' said Ian Buck, general manager and vice president of Accelerated Computing at NVIDIA. 'AI inference is a tremendous computational challenge.

Today NVIDIA introduced Jetson Xavier NX, "the world's smallest, most powerful AI supercomputer for robotic and embedded computing devices at the edge." With a compact form factor smaller than the size of a credit card, the energy-efficient Jetson Xavier NX module delivers server-class performance up to 21 TOPS for running modern AI workloads, and consumes as little as 10 watts of power. Jetson Xavier NX opens the door for embedded edge computing devices that demand increased performance but are constrained by size, weight, power budgets or cost. These include small commercial robots, drones, intelligent high-resolution sensors for factory logistics and production lines, optical inspection, network video recorders, portable medical devices and other industrial IoT systems. AI has become the enabling technology for modern robotics and embedded devices that will transform industries," said Deepu Talla, vice president and general manager of Edge Computing at NVIDIA. "Many of these devices, based on small form factors and lower power, were constrained from adding more AI features.