tag: deep learning

Accelerator Architecture For In-Memory Computation of CNN Inferences Using Racetrack Memory
By Technical Paper Link - 03 Jul, 2025 - Comments: 0

A new technical paper titled "Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems" was published by researchers at National University of Singapore, A*STAR, Chinese Academy of Sciences, and Hong Kong University of Science and Technology. Abstract "Deep neural networks generate and process large volumes of data, posing challe... » read more

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing (Infineon, U. Padova et al.)
By Technical Paper Link - 21 Jun, 2025 - Comments: 0

A new technical paper titled "Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing" was published by researchers at Infineon Technologies, University of Padova and University of Bologna. Abstract "In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant marke... » read more

Overview Of 103 Research Papers On Automatic SEM Image Analysis Algorithms For Semiconductor Defect Inspection (KU Leuven, Imec)
By Technical Paper Link - 20 May, 2025 - Comments: 0

A new technical paper titled "Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: a systematic review" was published by researchers at KU Leuven and imec. "We identified, categorized, and discussed automatic defect inspection algorithms that analyze scanning electron microscopy (SEM) images for semiconductor manufacturing (SM). This is a topic of c... » read more

Energy-Aware DL: The Interplay Between NN Efficiency And Hardware Constraints (Imperial College London, Cambridge)
By Technical Paper Link - 20 May, 2025 - Comments: 0

A new technical paper titled "Energy-Aware Deep Learning on Resource-Constrained Hardware" was published by researchers at Imperial College London and University of Cambridge. Abstract "The use of deep learning (DL) on Internet of Things (IoT) and mobile devices offers numerous advantages over cloud-based processing. However, such devices face substantial energy constraints to prolong batte... » read more

A Universal Deep Learning Model For Segmenting Automated Optical Inspection Images
By Nordson Test And Inspection - 08 Apr, 2025 - Comments: 0

A new technical paper titled "A Universal AI-Powered Segmentation Model for PCBA and Semiconductor" was published by researchers at Nordson Corporation. "This paper introduces a novel universal deep learning model designed to segment AOI images for both PCBA and 17 semiconductor components, offering a more robust and adaptable solution for defect detection," states the paper. Read more he... » read more

Mixed-Precision DL Inference, Co-Designed With HW Accelerator DPU (Intel)
By Technical Paper Link - 03 Feb, 2025 - Comments: 0

A new technical paper titled "StruM: Structured Mixed Precision for Efficient Deep Learning Hardware Codesign" was published by Intel. Abstract "In this paper, we propose StruM, a novel structured mixed-precision-based deep learning inference method, co-designed with its associated hardware accelerator (DPU), to address the escalating computational and memory demands of deep learning worklo... » read more

Monitor Etch Defects on Dies in the Outer Regions Of The Wafer Using ISR
By Technical Paper Link - 26 Nov, 2024 - Comments: 0

A technical paper titled "Detection of defective chips from nanostructures with a high-aspect ratio using hyperspectral imaging and deep learning" was published by researchers at Samsung Electronics. Abstract: "We have developed an imaging spectroscopic reflectometry (ISR) method based on hyperspectral imaging and deep learning to detect defects in the bottom region of high-aspect-ratio nan... » read more

Analog In-Memory Computing: Fast Deep NN Training (IBM Research)
By Technical Paper Link - 10 Sep, 2024 - Comments: 0

A new technical paper titled "Fast and robust analog in-memory deep neural network training" was published by researchers at IBM Research. Abstract "Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has... » read more

DL Compiler for Efficiently Utilizing Inter-Core Connected AI Chips (UIUC, Microsoft)
By Technical Paper Link - 28 Aug, 2024 - Comments: 0

A new technical paper titled "Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor" was published by researchers at UIUC and Microsoft Research. Abstract "As AI chips incorporate numerous parallelized cores to scale deep learning (DL) computing, inter-core communication is enabled recently by employing high-bandwidth and low-latency interconnect links on th... » read more

Co-optimizing HW Architecture, Memory Footprint, Device Placement And Per-Chip Operator Scheduling (Georgia Tech, Microsoft)
By Technical Paper Link - 28 Jul, 2024 - Comments: 0

A technical paper titled “Integrated Hardware Architecture and Device Placement Search” was published by researchers at Georgia Institute of Technology and Microsoft Research. Abstract: "Distributed execution of deep learning training involves a dynamic interplay between hardware accelerator architecture and device placement strategy. This is the first work to explore the co-optimization ... » read more

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