tag: convolutional neural network

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

Using Automatic Defect Classification To Reduce The Escape Rate Of Defects
By Bryce Chi - 09 Apr, 2024 - Comments: 0

Automated optical inspection (AOI) is a cornerstone in semiconductor manufacturing, assembly and testing facilities, and as such, it plays a crucial role in yield management and process control. Traditionally, AOI generates millions of defect images, all of which are manually reviewed by operators. This process is not only time-consuming but error prone due to human involvement and fatigue, whi... » read more

Design Optimization Of Split-Gate NOR Flash For Compute-In-Memory
By Technical Paper Link - 19 Sep, 2023 - Comments: 0

A technical paper titled “Design Strategies of 40 nm Split-Gate NOR Flash Memory Device for Low-Power Compute-in-Memory Applications” was published by researchers at Seoul National University of Science and Technology and University of Seoul. Abstract: "The existing von Neumann architecture for artificial intelligence (AI) computations suffers from excessive power consumption and memo... » read more

(Vision) Transformers: Rise Of The Chimera
By Jon Bunting - 20 Jun, 2023 - Comments: 0

It’s 2023 and transformers are having a moment. No, I’m not talking about the latest installment of the Transformers movie franchise, "Transformers: Rise of the Beasts"; I’m talking about the deep learning model architecture class, transformers, that is fueling anticipation, excitement, fear, and investment in AI. Transformers are not so new in the world of AI anymore; they were first ... » read more

Nightmare Fuel: The Hazards Of ML Hardware Accelerators
By Steve Roddy - 13 Apr, 2023 - Comments: 0

A major design challenge facing numerous silicon design teams in 2023 is building the right amount of machine learning (ML) performance capability into today’s silicon tape out in anticipation of what the state of the art (SOTA) ML inference models will look like in 2026 and beyond when that silicon will be used in devices in volume production. Given the continuing rapid rate of change in mac... » read more

Achieving Greater Accuracy In Real-Time Vision Processing With Transformers
By Gordon Cooper - 10 Jan, 2023 - Comments: 0

Transformers, first proposed in a Google research paper in 2017, were initially designed for natural language processing (NLP) tasks. Recently, researchers applied transformers to vision applications and got interesting results. While previously, vision tasks had been dominated by convolutional neural networks (CNNs), transformers have proven surprisingly adaptable to vision tasks like image cl... » read more

Artificial intelligence deep learning for 3D IC reliability prediction
By Technical Paper Link - 25 Apr, 2022 - Comments: 0

New research from National Yang Ming Chiao Tung University, National Center for High-Performance Computing (Taiwan), Tunghai University, MA-Tek Inc, and UCLA. Abstract "Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore’s law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly infl... » read more

Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data
By Technical Paper Link - 18 Apr, 2022 - Comments: 0

Abstract "In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a bro... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing
By Technical Paper Link - 17 Nov, 2021 - Comments: 0

Abstract: "In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of a... » read more

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