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

Xilinx AI Engines And Their Applications

By Xilinx - 31 Mar, 2021 - Comments: 0

This white paper explores the architecture, applications, and benefits of using Xilinx's new AI Engine for compute intensive applications like 5G cellular and machine learning DNN/CNN. 5G requires between five to 10 times higher compute density when compared with prior generations; AI Engines have been optimized for DSP, meeting both the throughput and compute requirements to deliver the hig... » read more

Fast, Low-Power Inferencing

By Russell Klein - 10 Dec, 2020 - Comments: 1

Power and performance are often thought of as opposing goals, opposite sides of the same coin if you will. A system can be run really fast, but it will burn a lot of power. Ease up on the accelerator and power consumption goes down, but so does performance. Optimizing for both power and performance is challenging. Inferencing algorithms for Convolutional Neural Networks (CNN) are compute int... » read more

The Winograd Transformation

By Ed Sperling - 07 Mar, 2019 - Comments: 0

Cheng Wang, senior vice president of engineering at Flex Logix, explains how the Winograd Transformation applies to convolutional neural networks. https://youtu.be/E7QJUby9x-I » read more

In-Memory Computing Challenges Come Into Focus

By Katherine Derbyshire - 06 Feb, 2019 - Comments: 6

For the last several decades, gains in computing performance have come by processing larger volumes of data more quickly and with superior precision. Memory and storage space are measured in gigabytes and terabytes now, not kilobytes and megabytes. Processors operate on 64-bit rather than 8-bit chunks of data. And yet the semiconductor industry’s ability to create and collect high quality ... » read more

AI Accelerator Gyrfalcon Soars Post Stealth

By Kevin Fogarty - 12 Nov, 2018 - Comments: 0

Milpitas, Calif.-based startup Gyrfalcon Technology Inc. (GTI), which emerged from semi-stealth mode in September, recently announced the datacenter-focused second generation of its neural-network accelerator, which was first aimed at the endpoint. GTI is not alone: The endpoint market is growing. By 2022, 25% of endpoint devices will execute AI algorithms (inference for neural network appli... » read more

Impact Of IP On AI SoCs

By Ron Lowman - 09 Aug, 2018 - Comments: 2

The combination of mathematics and processing capability has set in motion a new generation of technology advancements with an entire new world of possibilities related to Artificial Intelligence. AI mimics human behavior using deep learning algorithms. Neural networks are what we define as deep learning, which is a subset of machine learning, which is yet a subset of AI, as shown in Figure 1. ... » read more

FPGAs Drive Deeper Into Cars

By Ann Mutschler - 09 Jul, 2018 - Comments: 3

FPGAs are reaching deeper and wider inside of automobiles, playing an increasingly important role across more systems within a vehicle as the electronic content continues to grow. The role of FPGAs in automotive cameras and sensors is already well established. But they also are winning sockets inside of a raft of new technologies, ranging from the AI systems that will become the central logi... » read more

Deep Learning Spreads

By Ed Sperling - 31 Jan, 2018 - Comments: 0

Deep learning is gaining traction across a broad swath of applications, providing more nuanced and complex behavior than machine learning offers today. Those attributes are particularly important for safety-critical devices, such as assisted or autonomous vehicles, as well as for natural language processing where a machine can recognize the intent of words based upon the context of a convers... » read more

System Bits: Jan. 2

By Ann Mutschler - 02 Jan, 2018 - Comments: 0

Robots imagine their future to learn By playing with objects and then imagining how to get the task done, UC Berkeley researchers have developed a robotic learning technology that enables robots to figure out how to manipulate objects they have never encountered before. The team expects this technology could help self-driving cars anticipate future events on the road and produce more intel... » read more

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Knowledge Centers
Entities, people and technologies explored

Shift Left Is The Tip Of The Iceberg

A transformative change is underway for semiconductor design and EDA. New languages, models, and abstractions will need to be created.

by Brian Bailey

Partitioning In The Chiplet Era

Understanding how chiplets interact under different workloads is critical to ensuring signal integrity and optimal performance in heterogeneous designs.

by Ann Mutschler

3.5D: The Great Compromise

Pros and cons of a middle-ground chiplet assembly that combines 2.5D and 3D-IC.

by Ed Sperling

AI’s Role In Chip Design Widens, Drawing In New Startups

Focus is on letting engineers do much more with the same or fewer resources — and less drudgery.

by Karen Heyman

Key Technologies To Extend EUV To 14 Angstroms

Alongside high-NA EUV will be better-performing photoresists, reduced roughness using passivation and etch, and lateral etching to reduce tip-to-tip dimensions.

by Laura Peters

What Comes After HBM For Chiplets

The standard for high-bandwidth memory limits design freedom at many levels, but that is required for interoperability. What freedoms can be taken from other functions to make chiplets possible?

by Brian Bailey

Memory Fundamentals For Engineers

eBook: Nearly everything you need to know about memory, including detailed explanations of the different types of memory; how and where these are used today; what's changing, which memories are successful and which ones might be in the future; and the limitations of each memory type.

by The SE Staff

Why Small Fab And Assembly Houses Are Thriving

Megafabs churning out the most advanced processors are not the only game in town.

by Bryon Moyer

tag: convolutional neural networks