tag: CNNs

Toward Democratized IC Design And Customized Computing
By Jason Cong - 28 Jun, 2022 - Comments: 0

Integrated circuit (IC) design is often considered a “black art,” restricted to only those with advanced degrees or years of training in electrical engineering. Given that the semiconductor industry is struggling to expand its workforce, IC design must be rendered more accessible. The benefit of customized computing General-purpose computers are widely used, but their performance improv... » read more

Technical Paper Round-up: June 14
By Linda Christensen - 14 Jun, 2022 - Comments: 0

New technical papers added to Semiconductor Engineering’s library this week. [table id=33 /] Semiconductor Engineering is in the process of building this library of research papers. Please send suggestions (via comments section below) for what else you’d like us to incorporate. If you have research papers you are trying to promote, we will review them to see if they are a good fit f... » read more

Deep Learning Applications For Material Sciences: Methods, Recent Developments
By Technical Paper Link - 10 Jun, 2022 - Comments: 0

New technical paper titled "Recent advances and applications of deep learning methods in materials science" from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory, Carnegie Mellon University, Northwestern University, and Columbia University. Abstract "Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning... » read more

Scalable Approach to Fabricate Memristor Arrays at Wafer-scale
By Technical Paper Link - 06 Jun, 2022 - Comments: 0

New technical paper titled "Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing" from researchers at National University of Singapore and Institute of High Performance Computing, Singapore. Abstract "Realization of high-density and reliable resistive random access memories based on two-dimensional semiconductors is crucial toward their develop... » read more

Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS
By Technical Paper Link - 15 Feb, 2022 - Comments: 0

Abstract: "Camera-based Deep Learning algorithms are increasingly needed for perception in Automated Driving systems. However, constraints from the automotive industry challenge the deployment of CNNs by imposing embedded systems with limited computational resources. In this paper, we propose an approach to embed a multi- task CNN network under such conditions on a commercial prototy... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing
By Technical Paper Link - 09 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 an ... » read more

Software-Hardware Co-Design Becomes Real
By Brian Bailey - 27 Sep, 2021 - Comments: 1

For the past 20 years, the industry has sought to deploy hardware/software co-design concepts. While it is making progress, software/hardware co-design appears to have a much brighter future. In order to understand the distinction between the two approaches, it is important to define some of the basics. Hardware/software co-design is essentially a bottom-up process, where hardware is deve... » read more

Challenges For New AI Processor Architectures
By Brian Bailey - 29 Jul, 2021 - Comments: 0

Investment money is flooding into the development of new AI processors for the data center, but the problems here are unique, the results are unpredictable, and the competition has deep pockets and very sticky products. The biggest issue may be insufficient data about the end market. When designing a new AI processor, every design team has to answer one fundamental question — how much flex... » read more

Challenges Of Edge AI Inference
By Vinay Mehta - 01 Jul, 2021 - Comments: 0

Bringing convolutional neural networks (CNNs) to your industry—whether it be medical imaging, robotics, or some other vision application entirely—has the potential to enable new functionalities and reduce the compute requirements for existing workloads. This is because a single CNN can replace more computationally expensive image processing, denoising, and object detection algorithms. Howev... » read more

There’s More To Machine Learning Than CNNs
By Bryon Moyer - 09 Jun, 2021 - Comments: 1

Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

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