tag: CPU

Understanding SLAM (Simultaneous Localization And Mapping)
By Ed Sperling - 19 Feb, 2020 - Comments: 0

Amol Borkar, senior product manager for AI and computer vision at Cadence, talks with Semiconductor Engineering about mapping and tracking the movement of an object in a scene, how to identify key corners in a frame, how probabilities of accuracy fit into the picture, how noise can affect that, and how to improve the performance and reduce power in these systems. » read more

Divided On System Partitioning
By Brian Bailey - 30 Jan, 2020 - Comments: 0

Building an optimal implementation of a system using a functional description has been an industry goal for a long time, but it has proven to be much more difficult than it sounds. The general idea is to take software designed to run on a processor and to improve performance using various types of alternative hardware. That performance can be specified in various ways and for specific applic... » read more

A New Dawn For IP
By Brian Bailey - 12 Dec, 2019 - Comments: 0

The IP industry is changing again. The concept started as build once, use everywhere, but today it is more like architect once, customize everywhere. Few designs can afford sub-optimal IP for their application. The need for customized IP is driven by both leading-edge designs and the trailing markets, although for different reasons. While this customization is causing IP companies to transfo... » read more

Multi-Patterning EUV Vs. High-NA EUV
By Mark LaPedus - 04 Dec, 2019 - Comments: 4

Foundries are finally in production with EUV lithography at 7nm, but chip customers must now decide whether to implement their next designs using EUV-based multiple patterning at 5nm/3nm or wait for a new single-patterning EUV system at 3nm and beyond. This scenario revolves around ASML’s current extreme ultraviolet (EUV) lithography tool (NXE:3400C) versus a completely new EUV system with... » read more

Making Sense Of Inferencing Options
By Ed Sperling - 19 Nov, 2019 - Comments: 0

Ian Bratt, fellow in Arm’s machine learning group, sheds light on all the different processing elements in machine learning, how different end user requirements affect those choices, why CPUs are a critical element in orchestrating what happens in these systems, and how power and software play into these choices. » read more

Making Sense Of ML Metrics
By Ed Sperling - 16 Oct, 2019 - Comments: 0

Steve Roddy, vice president of products for Arm’s Machine Learning Group, talks with Semiconductor Engineering about what different metrics actually mean, and why they can vary by individual applications and use cases. » read more

More Data, More Processing, More Chips
By Ed Sperling - 07 Oct, 2019 - Comments: 0

Simon Segars, CEO of Arm, sat down with Semiconductor Engineering to talk about the impact of heterogeneous computing and new packaging approaches on IP, the need for more security, and how 5G and the edge will impact compute architectures and the chip industry. SE: There are a whole bunch of new markets opening up. How does Arm plan to tackle those? Segars: Luckily for us, we can design ... » read more

Where Is The Edge?
By Ed Sperling - 02 Oct, 2019 - Comments: 0

Mike Fitton, senior director of strategic planning at Achronix, talks about what the edge will look like, how that fits in with the cloud, what the requirements are both for processing and for storage, and how this concept will evolve.   Edge Knowledge Center Top stories, videos, blogs, white papers all related to the Edge » read more

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

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