tag: AI

How Much Power Will AI Chips Use?
By Ed Sperling - 12 Mar, 2020 - Comments: 1

AI and machine learning have voracious appetites when it comes to power. On the training side, they will fully utilize every available processing element in a highly parallelized array of processors and accelerators. And on the inferencing side they, will continue to optimize algorithms to maximize performance for whatever task a system is designed to do. But as with cars, mileage varies gre... » read more

Packaging And Package Design For AI At The Edge
By Andy Heinig - 12 Mar, 2020 - Comments: 0

Industrial applications will acquire significantly more data directly from machines in coming years. To properly handle this increase in data, it must already be prepared at the machine. The data of the individual sensors can be processed, or an initial data merger can take place here at the so-called “edge.” Algorithms and methods from the field of artificial intelligence increasingly a... » read more

HBM2E Memory: A Perfect Fit For AI/ML Training
By Frank Ferro - 12 Mar, 2020 - Comments: 0

Artificial Intelligence/Machine Learning (AI/ML) growth proceeds at a lightning pace. In the past eight years, AI training capabilities have jumped by a factor of 300,000 (10X annually), driving rapid improvements in every aspect of computing hardware and software. Memory bandwidth is one such critical area of focus enabling the continued growth of AI. Introduced in 2013, High Bandwidth Memo... » read more

Power Management Becomes Top Issue Everywhere
By Ann Mutschler - 12 Mar, 2020 - Comments: 1

Power management is becoming a bigger challenge across a wide variety of applications, from consumer products such as televisions and set-top-boxes to large data centers, where the cost of cooling server racks to offset the impact of thermal dissipation can be enormous. Several years ago, low-power design was largely relegated to mobile devices that were dependent on a battery. Since then, i... » read more

Power Challenges In ML Processors
By Brian Bailey - 12 Mar, 2020 - Comments: 0

The design of artificial intelligence (AI) chips or machine learning (ML) systems requires that designers and architects use every trick in the book and then learn some new ones if they are to be successful. Call it style, call it architecture, there are some designs that are just better than others. When it comes to power, there are plenty of ways that small changes can make large differences.... » read more

AI Chip DFT Techniques For Aggressive Time-To-Market
By Mentor, a Siemens Business - 10 Mar, 2020 - Comments: 0

AI chips have aggressive time-to-market goals. Designers can shave significant time off of DFT and silicon bring up using the techniques described in this paper. Leading AI semiconductor companies have already had success with Tessent DFT tools. To read more, click here. » read more

AI Roadmap: A human-centric approach to AI in aviation
By Technical Paper Link - 09 Mar, 2020 - Comments: 0

Source: EASA European Union Aviation Safety Agency February 2020 "EASA published its Artificial Intelligence Roadmap 1.0 which establishes the Agency’s initial vision on the safety and ethical dimensions of development of AI in the aviation domain. The AI Roadmap 1.0 is to be viewed as a starting point, intended to serve as a basis for discussion with the Agency’s stakeholders. It... » read more

High-Performance Memory For AI And HPC
By Ed Sperling - 09 Mar, 2020 - Comments: 0

Frank Ferro, senior director of product management at Rambus, examines the current performance bottlenecks in high-performance computing, drilling down into power and performance for different memory options, and explains what are the best solutions for different applications and why. » read more

Implementing Strong Security For AI/ML Accelerators
By Paul Karazuba - 05 Mar, 2020 - Comments: 0

A number of critical security vulnerabilities affecting high-performance CPUs identified in recent years have rocked the semiconductor industry. These high-profile vulnerabilities inadvertently allowed malicious programs to access sensitive data such as passwords, secret keys and other secure assets. The real-world risks of silicon complexity The above-mentioned vulnerabilities are primaril... » read more

HBM2E and GDDR6: Memory Solutions for AI
By Rambus - 04 Mar, 2020 - Comments: 0

Artificial Intelligence/Machine Learning (AI/ML) growth proceeds at a lightning pace. In the past eight years, AI training capabilities have jumped by a factor of 300,000 driving rapid improvements in every aspect of computing hardware and software. Meanwhile, AI inference is being deployed across the network edge and in a broad spectrum of IoT devices including in automotive/ADAS. Training and... » read more

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