Blog Topics

May 4, 2017

Imagination to slim down to graphics core

Hit by the loss of major client Apple, Imagination Technologies plans to sell off its MIPS and Ensigma divisions. The move signals a shift away from previous plans to diversify out from graphics processors.
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May 3, 2017

Master the verification challenge of PCIe-based NVMe storage

NVMe is driving the SSD market thanks to its many useful features, but at least five major challenges must inform your verification plan.
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May 2, 2017

Wally Rhines looks beyond ‘endless verification’ to the system era

DVCon China saw Mentor's chairman and CEO give a typically thorough keynote on the evolving challenges for verification.
May 1, 2017

Cadence tunes fixed-point DSP for neural networks

Cadence has stripped out some of the image-processing functions of the Vision P6 and boosted the number of execution units to build a DSP aimed at deep learning.
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April 28, 2017

ARM deploys data-center tech to study verification patterns

ARM is using technologies such as Hadoop and Spark to provide insight into how well its verification processes are working.
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April 18, 2017

Conference to look at agile development for embedded

A one-day conference on using agile development techniques for embedded systems takes place in the UK on 3 May.
April 13, 2017

DVCon China looms as submission deadline for Europe approaches

The first Chinese edition of Accellera's conference series takes place in Shanghai next Wednesday (April 19).
April 12, 2017

Cadence cuts up DRC for speed

Cadence Design Systems has launched a design-rule checking engine that can distribute its workload across multiple servers in a cloud, private farm or mixture of both to speed up signoff.
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April 6, 2017

Leverage AI and centralized processing for L-5 autonomous vehicles

L-5 autonomous vehicles need centralized raw data analysis with machine learning to cope with the demands of ASIL-D functional safety. Mentor's response is DRS360.
April 6, 2017

Solido sets up lab to drive machine-learning adoption

Solido aims to bring the types of machine-learning techniques the company has used for its physical-analysis tools to a wider range of EDA tools through the launch of its ML Labs initiative.

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