A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog IMC (IBM and ETH Zurich)


A technical paper titled “A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing” was published by researchers at IBM Research Europe and IIS-ETH Zurich. Abstract: "Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is requ... » read more

Fabs Begin Ramping Up Machine Learning


Fabs are beginning to deploy machine learning models to drill deep into complex processes, leveraging both vast compute power and significant advances in ML. All of this is necessary as dimensions shrink and complexity increases with new materials and structures, processes, and packaging options, and as demand for reliability increases. Building robust models requires training the algorithms... » read more

Deep Learning Discovers Millions Of New Materials (Google)


A technical paper titled “Scaling deep learning for materials discovery” was published by researchers at Google DeepMind and Google Research. Abstract: "Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottleneck... » read more

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

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