tag: CPU

Machine Learning Inferencing At The Edge
By Ed Sperling - 12 Sep, 2019 - Comments: 1

Ian Bratt, fellow in Arm's machine learning group, talks about why machine learning inferencing at the edge is so difficult, what are the tradeoffs, how to optimize data movement, how to accelerate that movement, and how it differs from developing other types of processors. » read more

What’s Powering Artificial Intelligence?
By Arm - 11 Sep, 2019 - Comments: 0

While artificial intelligence (AI) and machine learning (ML) applications soar in popularity, many organizations are questioning where ML workloads should be performed. Should they be done on a central processor (CPU), a graphics processor (GPU), or a neural processor (NPU)? The choice most teams are making today will surprise you. To scale artificial intelligence (AI) and machine learning (... » read more

Week in Review – IoT, Security, Autos
By Jeff Dorsch - 06 Sep, 2019 - Comments: 0

Products/Services Rambus entered an exclusive agreement to acquire the Silicon IP, Secure Protocols, and Provisioning business from Verimatrix, formerly known as Inside Secure. Financial terms were not revealed. The transaction is expected to close this year. Rambus will use the Verimatrix offerings in such demanding applications as artificial intelligence, automotive, the Internet of Things, ... » read more

The Race For Better Computational Software
By Ed Sperling - 05 Sep, 2019 - Comments: 0

Anirudh Devgan, president of Cadence, sat down with Semiconductor Engineering to talk about computational software, why it's so critical at the edge and in AI systems, and where the big changes are across the semiconductor industry. What follows are excerpts of that conversation. SE: There is no consistent approach to how data will be processed at the edge, in part because there is no consis... » read more

Chiplets, Faster Interconnects, More Efficiency
By Ed Sperling - 22 Aug, 2019 - Comments: 1

Big chipmakers are turning to architectural improvements such as chiplets, faster throughput both on-chip and off-chip, and concentrating more work per operation or cycle, in order to ramp up processing speeds and efficiency. Taken as a whole, this represents a significant shift in direction for the major chip companies. All of them are wrestling with massive increases in processing demands ... » read more

Why Scaling Must Continue
By Ed Sperling - 15 Aug, 2019 - Comments: 3

The entire semiconductor industry has come to the realization that the economics of scaling logic are gone. By any metric—price per transistor, price per watt, price per unit area of silicon—the economics are no longer in the plus column. So why continue? The answer is more complicated than it first appears. This isn't just about inertia and continuing to miniaturize what was proven in t... » read more

Powering The Edge: Driving Optimal Performance With the Arm ML Processor
By Arm - 07 Aug, 2019 - Comments: 0

On-device machine learning (ML) processing is already happening in more than 4 billion smart phones. As the adoption of connected devices continues to grow exponentially, the resulting data explosion means cloud processing could soon become an expensive and high-latency luxury. The Arm ML processor is defining the future of ML inference at the edge, allowing smart devices to make independent... » read more

Where Should Auto Sensor Data Be Processed?
By Ann Mutschler - 01 Aug, 2019 - Comments: 0

Fully autonomous vehicles are coming, but not as quickly as the initial hype would suggest because there is a long list of technological issues that still need to be resolved. One of the basic problems that still needs to be solved is how to process the tremendous amount of data coming from the variety of sensors in the vehicle, including cameras, radar, LiDAR and sonar. That data is the dig... » read more

Semiconductor’s Dinosaurs
By Brian Bailey - 25 Jul, 2019 - Comments: 2

Dinosaurs once ruled this planet. They existed in every shape and form – some large, others tiny. Each adapted to its own specific environment. Some stayed on the land, others went to sea, and yet another group took to the skies. They looked like they were invincible and would be the pinnacle of the food chain. Then a cataclysmic event happened, and dinosaurs went into a fairly rapid decline.... » read more

Power Is Limiting Machine Learning Deployments
By Brian Bailey - 25 Jul, 2019 - Comments: 3

The total amount of power consumed for machine learning tasks is staggering. Until a few years ago we did not have computers powerful enough to run many of the algorithms, but the repurposing of the GPU gave the industry the horsepower that it needed. The problem is that the GPU is not well suited to the task, and most of the power consumed is waste. While machine learning has provided many ... » read more

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