tag: CNN

Multi-Core Architecture Optimized For Time-Predictable Neural Network Inference (FZI, KIT)
By Technical Paper Link - 21 Nov, 2025 - Comments: 0

A new technical paper titled "MultiVic: A Time-Predictable RISC-V Multi-Core Processor Optimized for Neural Network Inference" was published by researchers at FZI Research Center for Information Technology and Karlsruhe Institute for Information Technology (KIT). Abstract: "Real-time systems, particularly those used in domains like automated driving, are increasingly adopting neural network... » read more

How Grinn And Synaptics Are Accelerating Edge AI Adoption And Innovation
By Synaptics - 09 Oct, 2025 - Comments: 0

From smart cities to industrial automation, organizations are rethinking how and where data is processed. The answer, increasingly, is at the Edge—where devices can analyze information in real time without sending it to the cloud. This approach improves responsiveness, enhances security, and reduces reliance on network connectivity. Recognizing this shift, Grinn Global and Synaptics have p... » read more

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

Machine Learning-Based IR Drop Prediction Approach
By Technical Paper Link - 25 Feb, 2025 - Comments: 0

A new technical paper titled "Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate" was published by researchers at KTH Royal Institute of Technology and Ericsson Research. ABSTRACT "Accurate estimation of voltage drop (IR drop) in modern Application-Specific Integrated Circuits (ASICs) is highly time and resource demanding, due to the growing complex... » read more

Using Deep Learning ADC For Defect Classification For Automatic Defect Inspection
By Onto Innovation - 14 May, 2024 - Comments: 0

In traditional semiconductor packaging, manual defect review after automated optical inspection (AOI) is an arduous task for operators and engineers, involving review of both good and bad die. It is hard to avoid human errors when reviewing millions of defect images every day, and as a result, underkill or overkill of die can occur. Automatic defect classification (ADC) can reduce the number of... » 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

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