tag: ML

Nvidia To Buy Arm For $40B
By Ed Sperling - 13 Sep, 2020 - Comments: 2

Nvidia inked a deal with Softbank to buy Arm for $40 billion, combining the No. 1 AI/ML GPU maker with the No. 1 processor IP company. Assuming the deal wins regulatory approval, the combination of these two companies will create a powerhouse in the AI/ML world. Nvidia's GPUs are the go-to platform for training algorithms, while Arm has a broad portfolio of AI/ML processor cores. Arm also ha... » read more

How ML Enables Cadence Digital Tools To Deliver Better PPA
By Cadence - 09 Sep, 2020 - Comments: 0

Artificial intelligence (AI) and machine learning (ML) are emerging as powerful new ways to do old things more efficiently, which is the benchmark that any new and potentially disruptive technology must meet. In chip design, results are measured in many different ways, but common metrics are power (consumed), performance (provided), and area (required), collectively referred to as PPA. These me... » read more

From Data Center To End Device: AI/ML Inferencing With GDDR6
By Rambus - 09 Sep, 2020 - Comments: 0

Created to support 3D gaming on consoles and PCs, GDDR packs performance that makes it an ideal solution for AI/ML inferencing. As inferencing migrates from the heart of the data center to the network edge, and ultimately to a broad range of AI-powered IoT devices, GDDR memory’s combination of high bandwidth, low latency, power efficiency and suitability for high-volume applications will be i... » read more

Intelligent System Design
By Cadence - 02 Sep, 2020 - Comments: 0

Electronics technology is proliferating to new, creative applications and appearing in our everyday lives. To compete, system companies are increasingly designing their own semiconductor chips, and semiconductor companies are delivering software stacks, to enable substantial differentiation of their products. This trend started in mobile devices and is now moving into cloud computing, automotiv... » read more

Getting Particular About Partitioning
By Ed Sperling - 31 Aug, 2020 - Comments: 0

Partitioning could well be one of the most important and pervasive trends since the invention of computers. It has been around for almost as long, too. The idea dates back at least as far back as the Manhattan Project during World War II, when computations were wrapped within computations. It continued from there with what we know as time-sharing, which rather crudely partitioned access by p... » read more

Chiplet Reliability Challenges Ahead
By Ed Sperling - 12 Aug, 2020 - Comments: 2

Assembling chips using LEGO-like hard IP is finally beginning to take root, more than two decades after it was first proposed, holding the promise of faster time to market with predictable results and higher yield. But as these systems of chips begin showing up in mission-critical and safety-critical applications, ensuring reliability is proving to be stubbornly difficult. The main driver fo... » read more

Performance Metrics For Convolutional Neural Network Accelerators
By Vinay Mehta - 06 Aug, 2020 - Comments: 0

Across the industry, there are few benchmarks that customers and potential end users can employ to evaluate an inference acceleration solution end-to-end. Early on in this space, the performance of an accelerator was measured as a single number: TOPs. However, the limitations of using a single number has been covered in detail in the past by previous blogs. Nevertheless, if the method of cal... » read more

Designer And IP Tracks Swell With Focus On ML, Security And Traditional EDA Methodologies
By Michelle Clancy - 13 Jul, 2020 - Comments: 0

What are designers keenly interested in as the 57th Design Automation Conference (DAC) approaches? If you said machine learning (ML), you’d be only partially right. Based on designer and IP tracks submissions to the 57th edition of the venerable electronics-industry event, ML – how to design with it and optimize EDA tools and flows using it – is a hot topic. But so too are more traditi... » read more

Machine Learning Enabled Root Cause Analysis For Low Power Verification
By Himanshu Bhatt - 13 Jul, 2020 - Comments: 0

By Himanshu Bhatt and Susantha Wijesekara Next-generation SoCs with advanced graphics, computing and artificial intelligence capabilities are posing unforeseen challenges in verification. Designers and verification engineers using static verification technologies for low power often see many violations in the initial stages. Efficient debugging and determining root cause is a real issue and ... » read more

Are Better Machine Training Approaches Ahead?
By Bryon Moyer - 01 Jul, 2020 - Comments: 1

We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

← Older posts Newer posts →

  • Sponsors

About

Navigation

Connect With Us

Copyright ©2013-2024 SMG   |  Terms of Service  |  Privacy Policy