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Visual Fault Inspection Using A Hybrid System Of Stacked DNNs


A technical paper titled "Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks" was published by researchers at Chemnitz University of Technology (Germany). According to the paper, "this contribution introduces a novel hybrid multistage system of stacked deep neural networks (SH-DNN) which allows the localization... » read more

New Uses For AI In Chips


Artificial intelligence is being deployed across a number of new applications, from improving performance and reducing power in a wide range of end devices to spotting irregularities in data movement for security reasons. While most people are familiar with using machine learning and deep learning to distinguish between cats and dogs, emerging applications show how this capability can be use... » read more

Simulation Framework to Evaluate the Feasibility of Large-scale DNNs based on CIM Architecture & Analog NVM


Technical paper titled "Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines" from researchers at UCLA. Abstract "Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNNs) to improve scalability, speed, and energy efficiency. Such architectures, however, leverage ... » read more

Neuromorphic Chips & Power Demands


Research paper titled "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," from researchers at Graz University of Technology and Intel Labs. Abstract "Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs. But this requires to understand how D... » read more

A novel multimodal hand database for biometric authentication


Abstract "Biometric authentication is one of the most exciting areas in the era of security. Biometric authentication ideally refers to the process of identifying or verifying the user through physiological and behavioral measurements using security processes. Multimodal biometrics are preferred over unimodal biometrics due to the defensive nature of multimodal biometrics. This research intr... » read more

NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark


Abstract:   "Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardw... » read more

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Power/Performance Bits: Nov. 17


NVMe controller for research Researchers at the Korea Advanced Institute of Science and Technology (KAIST) developed a non-volatile memory express (NVMe) controller for storage devices and made it freely available to universities and research institutions in a bid to reduce research costs. Poor accessibility of NVMe controller IP is hampering academic and industrial research, the team argue... » read more

Neuromorphic Computing Drives The Landscape Of Emerging Memories For Artificial Intelligence SoCs


The pace of deep machine learning and artificial intelligence (AI) is changing the world of computing at all levels of hardware architecture, software, chip manufacturing, and system packaging. Two major developments have opened the doors to implementing new techniques in machine learning. First, vast amounts of data, i.e., “Big Data,” are available for systems to process. Second, advanced ... » read more

Priorities Shift In IC Design


The rush to the edge and new applications around AI are causing a shift in design strategies toward the highest performance per watt, rather than the highest performance or lowest power. This may sound like hair-splitting, but it has set a scramble in motion around how to process more data more quickly without just relying on faster processors and accelerators. Several factors are driving th... » read more

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