317 research outputs found
Locality-Sensitive Hashing with Margin Based Feature Selection
We propose a learning method with feature selection for Locality-Sensitive
Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays.
These bit arrays can be used to perform similarity searches and personal
authentication. The proposed method uses bit arrays longer than those used in
the end for similarity and other searches and by learning selects the bits that
will be used. We demonstrated this method can effectively perform optimization
for cases such as fingerprint images with a large number of labels and
extremely few data that share the same labels, as well as verifying that it is
also effective for natural images, handwritten digits, and speech features.Comment: 9 pages, 6 figures, 3 table
A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems
The 3D reconstruction of simultaneous localization and mapping (SLAM) is an
important topic in the field for transport systems such as drones, service
robots and mobile AR/VR devices. Compared to a point cloud representation, the
3D reconstruction based on meshes and voxels is particularly useful for
high-level functions, like obstacle avoidance or interaction with the physical
environment. This article reviews the implementation of a visual-based 3D scene
reconstruction pipeline on resource-constrained hardware platforms. Real-time
performances, memory management and low power consumption are critical for
embedded systems. A conventional SLAM pipeline from sensors to 3D
reconstruction is described, including the potential use of deep learning. The
implementation of advanced functions with limited resources is detailed. Recent
systems propose the embedded implementation of 3D reconstruction methods with
different granularities. The trade-off between required accuracy and resource
consumption for real-time localization and reconstruction is one of the open
research questions identified and discussed in this paper
Gem5Pred: Predictive Approaches For Gem5 Simulation Time
Gem5, an open-source, flexible, and cost-effective simulator, is widely
recognized and utilized in both academic and industry fields for hardware
simulation. However, the typically time-consuming nature of simulating programs
on Gem5 underscores the need for a predictive model that can estimate
simulation time. As of now, no such dataset or model exists. In response to
this gap, this paper makes a novel contribution by introducing a unique dataset
specifically created for this purpose. We also conducted analysis of the
effects of different instruction types on the simulation time in Gem5. After
this, we employ three distinct models leveraging CodeBERT to execute the
prediction task based on the developed dataset. Our superior regression model
achieves a Mean Absolute Error (MAE) of 0.546, while our top-performing
classification model records an Accuracy of 0.696. Our models establish a
foundation for future investigations on this topic, serving as benchmarks
against which subsequent models can be compared. We hope that our contribution
can simulate further research in this field. The dataset we used is available
at https://github.com/XueyangLiOSU/Gem5Pred
OSMOSIS: Enabling Multi-Tenancy in Datacenter SmartNICs
Multi-tenancy is essential for unleashing SmartNIC's potential in
datacenters. Our systematic analysis in this work shows that existing on-path
SmartNICs have resource multiplexing limitations. For example, existing
solutions lack multi-tenancy capabilities such as performance isolation and QoS
provisioning for compute and IO resources. Compared to standard NIC data paths
with a well-defined set of offloaded functions, unpredictable execution times
of SmartNIC kernels make conventional approaches for multi-tenancy and QoS
insufficient. We fill this gap with OSMOSIS, a SmartNICs resource manager
co-design. OSMOSIS extends existing OS mechanisms to enable dynamic hardware
resource multiplexing on top of the on-path packet processing data plane. We
implement OSMOSIS within an open-source RISC-V-based 400Gbit/s SmartNIC. Our
performance results demonstrate that OSMOSIS fully supports multi-tenancy and
enables broader adoption of SmartNICs in datacenters with low overhead.Comment: 12 pages, 14 figures, 103 reference
Dagstuhl News January - December 2000
"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic
Integrating Blockchains and Intelligent Agents in the Pursuit of Artificial General Intelligence
Artificial General Intelligence (AGI) is the next greatest technological milestone. AGI can be defined as a realized artificial intelligence (AI) with the ability to understand and solve problems of various scope within constantly changing environments. To take steps toward this goal, a baseline of information will be provided regarding surrounding topics and the current state of AGI, itself. Through the culmination of swarms of highly optimized narrow AI agents, a collaborative effort will be extended toward general intelligence. Blockchains have been selected to facilitate this connection. A software deliverable will accompany this thesis to illustrate how this idea might be realized. The SingularityNET platform is utilized for this end due to its advanced protocols and methods for inter-AI communication
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