category: AI/ML/DL

category: IoT, Security & Automotive

New Ways To Optimize GEMM-Based Applications Targeting Two Leading AI-Optimized FPGA Architectures
By Technical Paper Link - 07 May, 2024 - Comments: 0

A technical paper titled “Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs” was published by researchers at The University of Texas at Austin and Arizona State University. Abstract: "FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading FPGA... » read more

Framework For Early Anomaly Detection In AMS Components Of Automotive SoCs
By Technical Paper Link - 30 Apr, 2024 - Comments: 0

A technical paper titled “Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning” was published by researchers at University of Texas at Dallas, Intel Corporation, NXP Semiconductors, and Texas Instruments. Abstract: "Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) ... » read more

Memristor Crossbar Architecture for Encryption, Decryption and More
By Technical Paper Link - 16 Apr, 2024 - Comments: 0

A new technical paper titled "Tunable stochastic memristors for energy-efficient encryption and computing" was published by researchers at Seoul National University, Sandia National Laboratories, Texas A&M University and Applied Materials. Abstract "Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirement... » read more

In-Memory Computing: Techniques for Error Detection and Correction
By Technical Paper Link - 16 Apr, 2024 - Comments: 0

A new technical paper titled "Error Detection and Correction Codes for Safe In-Memory Computations" was published by researchers at Robert Bosch, Forschungszentrum Julich, and Newcastle University. Abstract "In-Memory Computing (IMC) introduces a new paradigm of computation that offers high efficiency in terms of latency and power consumption for AI accelerators. However, the non-idealities... » read more

High-NA EUVL: Automated Defect Inspection Based on SEMI-SuperYOLO-NAS
By Technical Paper Link - 10 Apr, 2024 - Comments: 0

A new technical paper titled "Towards Improved Semiconductor Defect Inspection for high-NA EUVL based on SEMI-SuperYOLO-NAS" was published by researchers at KU Leuven, imec, Ghent University, and SCREEN SPE. Abstract "Due to potential pitch reduction, the semiconductor industry is adopting High-NA EUVL technology. However, its low depth of focus presents challenges for High Volume Manufac... » read more

Optimizing Event-Based Neural Network Processing For A Neuromorphic Architecture
By Technical Paper Link - 28 Mar, 2024 - Comments: 0

A new technical paper titled "Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration" was published by imec, TU Delft and University of Twente. Abstract "Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still str... » read more

Verifying Hardware CWEs in RTL Designs Generated by GenAI
By Technical Paper Link - 27 Mar, 2024 - Comments: 0

A new technical paper titled "All Artificial, Less Intelligence: GenAI through the Lens of Formal Verification" was published by researchers at Infineon Technologies. Abstract "Modern hardware designs have grown increasingly efficient and complex. However, they are often susceptible to Common Weakness Enumerations (CWEs). This paper is focused on the formal verification of CWEs in a dataset... » read more

Designing AI Hardware To Deal With Increasingly Challenging Memory Wall (UC Berkeley)
By Technical Paper Link - 25 Mar, 2024 - Comments: 0

A new technical paper titled "AI and Memory Wall" was published by researchers at UC Berkeley, ICSI, and LBNL. Abstract "The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memo... » read more

HW Implementation of Memristive ANNs
By Technical Paper Link - 22 Mar, 2024 - Comments: 0

A new technical paper titled "Hardware implementation of memristor-based artificial neural networks" was published by KAUST, Universitat Autònoma de Barcelona, IBM Research, USC, University of Michigan and others. Abstract: "Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units oper... » read more

Transformer Model Based Clustering Methodology For Standard Cell Layout Automation (Nvidia)
By Technical Paper Link - 19 Mar, 2024 - Comments: 0

A new technical paper titled "Novel Transformer Model Based Clustering Method for Standard Cell Design Automation" was published by researchers at Nvidia. Abstract "Standard cells are essential components of modern digital circuit designs. With process technologies advancing beyond 5nm, more routability issues have arisen due to the decreasing number of routing tracks (RTs), increasing numb... » read more

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