Center For Deep Learning In Electronics Manufacturing: Bringing Deep Learning To Production For Photomask Manufacturing

The Center for Deep Learning in Electronics Manufacturing (CDLe) was formed as an alliance between D2S, Mycronic and NuFlare Technology in autumn 2018. Assignees from each alliance partner worked with deep learning (DL) experts under the leadership of Ajay Baranwal, director of CDLe. The CDLe’s mission was to 1) turn DL into a core competency inside each of the companies and 2) do DL projects... » read more

Analog In-Memory Cores With Multi-Memristive Unit-Cells (IBM)

A technical paper titled “Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing” was published by researchers at IBM Research-Europe, IBM Research-Albany, and IBM Research-Yorktown Heights. Abstract: "Analog in-memory computing (AIMC) using memristive devices is considered a promising Non-von Neumann approach for deep learning (DL... » read more

GNN-Based Pre-Silicon Power Side-Channel Analysis Framework At RTL Level

A technical paper titled “SCAR: Power Side-Channel Analysis at RTL-Level” was published by researchers at University of Texas at Dallas, Technology Innovation Institute and University of Illinois Chicago. Abstract: "Power side-channel attacks exploit the dynamic power consumption of cryptographic operations to leak sensitive information of encryption hardware. Therefore, it is necessary t... » read more

Using Deep Learning to Secure The CAN Bus From Advanced Intrusion Attacks

A technical paper titled “CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level” was published by researchers at Virginia Tech and others. "As modern vehicles become more connected to external networks, the attack surface of the CAN bus system grows drastically. To secure the CAN bus from advanced intrusion attacks, we propose a sig... » read more

When And Where To Implement AI/ML In Fabs

Deciphering complex interactions between variables is where machine learning and deep learning shine, but figuring out exactly how ML-based systems will be most useful is the job of engineers. The challenge is in pairing their domain expertise with available ML tools to maximize the value of both. This depends on sufficient quantities of good data, highly optimized algorithms, and proper tra... » read more

Automotive Intrusion Detection Methodologies (TU Denmark)

A new technical paper titled "Intrusion Detection in the Automotive Domain: A Comprehensive Review" was published by researchers at DTU Compute Technical University of Denmark Abstract "The automotive domain has realized amazing advancements in communication, connectivity, and automation— and at a breakneck pace. Such advancements come with ample benefits, such as the reduction of traffic... » read more

Chiplets: Bridging The Gap Between The System Requirements And Design Aggregation, Planning, And Optimization

A technical paper titled “System and Design Technology Co-optimization of Chiplet-based AI Accelerator with Machine Learning” was published by researchers at Auburn University. Abstract: "With the availability of advanced packaging technology and its attractive features, the chiplet-based architecture has gained traction among chip designers. The large design space and the lack of sys... » read more

A PIM Architecture That Supports Floating Point-Precision Computations Within The Memory Chip

A technical paper titled “FlutPIM: A Look-up Table-based Processing in Memory Architecture with Floating-point Computation Support for Deep Learning Applications” was published by researchers at Rochester Institute of Technology and George Mason University. Abstract: "Processing-in-Memory (PIM) has shown great potential for a wide range of data-driven applications, especially Deep Learnin... » read more

Improving Image Resolution At The Edge

How much cameras see depends on how accurately the images are rendered and classified. The higher the resolution, the greater the accuracy. But higher resolution also requires significantly more computation, and it requires flexibility in the design to be able to adapt to new algorithms and network models. Jeremy Roberson, technical director and software architect for AI/ML at Flex Logix, talks... » read more

RL-Guided Detailed Routing Framework for Advanced Custom Circuits

A technical paper titled "Reinforcement Learning Guided Detailed Routing for Custom Circuits" was published by researchers at UT Austin, Princeton University, and NVIDIA. "This paper presents a novel detailed routing framework for custom circuits that leverages deep reinforcement learning to optimize routing patterns while considering custom routing constraints and industrial design rules. C... » read more

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