Performance Of Analog In-Memory Computing On Imaging Problems


A technical paper titled "Accelerating AI Using Next-Generation Hardware: Possibilities and Challenges With Analog In-Memory Computing" was published by researchers at Lund University and Ericsson Research. Abstract "Future generations of computing systems need to continue increasing processing speed and energy efficiency in order to meet the growing workload requirements under stringent en... » read more

Issues And Challenges In Super-Resolution Object Detection And Recognition


If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

Working With The NimbleAI Project To Push The Boundaries Of Neuromorphic Vision


At the end of 2022, the EU kicked off a cool project that aims to implement neuromorphic vision. But what is that? Let’s take a deeper look at the project and our contribution. First, if you are not familiar with Codasip Labs, I want to mention this briefly. Codasip Labs is in fact our innovation hub where we explore new technologies and try to contribute to the technology of the future. ... » read more

Multiexpert Adversarial Regularization For Robust And Data-Efficient Deep Supervised Learning


Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks, the lack of enough labeled images in the training set often results in overfitting. Another issue is the mismatch between the training and the test domains, which resul... » read more

AI Feeds Vision Processor, Image Sensor Boom


Vision systems are rapidly becoming ubiquitous, driven by big improvements in image sensors as well as new types of sensors. While the sensor itself often is developed using mature-node silicon, increasingly it is connected to vision processors developed at the most advanced process nodes. That allows for the highest performance per watt, and it also allows designs to incorporate AI accelera... » read more

Publicly Available Dataset for PCB X-Ray Inspection (FICS- University of Florida)


Researchers from the Florida Institute for Cybersecurity (FICS) at the University of Florida published this technical paper titled "FICS PCB X-ray: A dataset for automated printed circuit board inter-layers inspection." Abstract "Advancements in computer vision and machine learning breakthroughs over the years have paved the way for automated X-ray inspection (AXI) of printed circuit bo... » read more

Finding Wafer Defects Using Quantum DL


New research paper titled "Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning" by researchers at National Tsing Hua University. Abstract "With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the develo... » read more

Deep Learning In Industrial Inspection


Deep learning is at the upper end of AI complexity, sifting through more data to achieve more accurate results. Charlie Zhu, vice president of R&D at CyberOptics, talks about how DL can be utilized with inspection to identify defects in chips that are not discernible by traditional computer vision algorithms, classifying multiple objects simultaneously from multiple angles and taking into accou... » read more

Data Fusion Scheme For Object Detection & Trajectory Prediction for Autonomous Driving


New research paper titled "Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving" from researchers at Uber. Abstract "We present an end-to-end method for object detection and trajectory prediction utilizing multi-view representations of LiDAR returns. Our method builds on a state-of-the-art Bird's-Eye View (BEV) network that fuses voxelized featur... » read more

Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS


Abstract: "Camera-based Deep Learning algorithms are increasingly needed for perception in Automated Driving systems. However, constraints from the automotive industry challenge the deployment of CNNs by imposing embedded systems with limited computational resources. In this paper, we propose an approach to embed a multi- task CNN network under such conditions on a commercial prototy... » read more

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