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ICE-SEARCH: A Language Model-Driven Feature Selection Approach
This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications.
A Comprehensive Review and Systematic Analysis of Artificial Intelligence Regulation Policies
This study, containing historical lessons and analysis methods, aims to help governing bodies untangling the AI regulatory chaos through a divide-and-conquer manner.
Autonomy 2.0: The Quest for Economies of Scale
With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines.
AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach
This paper introduces Artificial Intelligence Clinics on Mobile (AICOM), an open-source project devoted to answering the United Nations Sustainable Development Goal 3 (SDG3) on health, which represents a universal recognition that health is fundamental to human capital and social and economic development.
Compliance Costs of AI Technology Commercialization: A Field Deployment Perspective
While Artificial Intelligence (AI) technologies are progressing fast, compliance costs have become a huge financial burden for AI startups, which are already constrained on research & development budgets.
Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators
As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy sensitive applications such as autonomous driving and biometric authentication, it is critical to understand the fault-tolerance nature of DNNs.
AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments
Also, through the AICOM-MP project, we have generalized a methodology of developing health AI technologies for AMCs to allow universal access even in resource-constrained environments.
Autonomous Mobile Clinics: Empowering Affordable Anywhere Anytime Healthcare Access
Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine.
Dilemma of the Artificial Intelligence Regulatory Landscape
As a startup company in the autonomous driving space, we have undergone four years of painful experiences dealing with a broad spectrum of regulatory requirements.
Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card
Autonomous driving is of great interest in both research and industry.
Dataflow Accelerator Architecture for Autonomous Machine Computing
Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing.
4C: A Computation, Communication, and Control Co-Design Framework for CAVs
Finally, we present several challenges to achieving the vision of the 4C framework.
Rise of the Autonomous Machines
After decades of uninterrupted progress and growth, information technology has so evolved that it can be said we are entering the age of autonomous machines, but there exist many roadblocks in the way of making this a reality.
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform
Stereo matching is a critical task for robot navigation and autonomous vehicles, providing the depth estimation of surroundings.
An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform
In our past few years' of commercial deployment experiences, we identify localization as a critical task in autonomous machine applications, and a great acceleration target.
Engineering Education in the Age of Autonomous Machines
In the past few years, we have observed a huge supply-demand gap for autonomous driving engineers.
Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines
We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe.
Self-Driving Cars
Hardware Architecture
A Survey of FPGA-Based Robotic Computing
On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios.
CoCoPIE: Making Mobile AI Sweet As PIE --Compression-Compilation Co-Design Goes a Long Way
Assuming hardware is the major constraint for enabling real-time mobile intelligence, the industry has mainly dedicated their efforts to developing specialized hardware accelerators for machine learning and inference.
CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles
Meanwhile, several pioneer efforts have focused on the edge computing system and architecture design for the CAVs scenario and provided various heterogeneous platform prototypes for CAVs.
Distributed, Parallel, and Cluster Computing Performance
Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines
In this paper, we present the Trifo Visual Inertial Odometry (Trifo-VIO), a tightly-coupled filtering-based stereo VIO system using both points and lines.
Teaching Autonomous Driving Using a Modular and Integrated Approach
Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other.
PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design
In this paper, we present the PerceptIn Robotics Vision System (PIRVS) system, a visual-inertial computing hardware with embedded simultaneous localization and mapping (SLAM) algorithm.
Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm
The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data.
Enabling Embedded Inference Engine with ARM Compute Library: A Case Study
Our conclusion is that, on embedded devices, we most likely will use very simple deep learning models for inference, and with well-developed building blocks such as ACL, it may be better in both performance and development time to build the engine from scratch.
