tag: machine learning

Blog Review: May 9
By Jesse Allen - 09 May, 2018 - Comments: 0

Mentor's Doug Amos explains the differences (and similarities) between verification and validation, why switching between engines needs to be simpler, and why the limits of verification are driving a growth in validation importance. Synopsys' Melissa Kirschner provides a primer on 5G and the five technologies that will need to work in tandem to bring the promised high speeds and low latency.... » read more

Power/Performance Bits: May 8
By Jesse Allen - 08 May, 2018 - Comments: 0

Cobalt-free cathodes Researchers at the University of California, Berkeley, built lithium-ion battery cathodes without cobalt that can store 50% more energy than traditional cobalt-containing cathodes. Currently, lithium-ion battery cathodes use layered structures, which cobalt is necessary to maintain. When lithium ions move from the cathode to anode during charging, a lot of space is left... » read more

Software-Defined Test And Measurement
By Jeff Dorsch - 07 May, 2018 - Comments: 0

Software-defined radios, instrumentation and test are ramping up alongside a flood of new technologies related to assisted and autonomous vehicles, 5G, and military/aerospace electronics, breathing new life and significant change into the test and measurement market. Software-defined test adds flexibility in markets where the products and protocols are evolving or still being defined, and wh... » read more

Rules Of The Driverless Road
By Ed Sperling - 03 May, 2018 - Comments: 0

The growing disparity among states, countries and carmakers over autonomous driving is turning what should be a logical progression into chaos. Consider what's happening in California, which is determined to remain the leader in this tech revolution. The state last month relaxed its testing rules so that cars can be monitored remotely, with no driver actually present inside the car. I... » read more

Challenges At The Edge
By Kevin Fogarty - 03 May, 2018 - Comments: 0

By Kevin Fogarty and Ed Sperling Edge computing is inching toward the mainstream as the tech industry begins grappling with the fact that far too much data will be generated by sensors to send everything back to the cloud for processing. The initial idea behind the IoT/IIoT, as well as other connected devices, was that simple sensors would relay raw data to the cloud for processing throug... » read more

Finding And Fixing ML’s Flaws
By Ed Sperling - 02 May, 2018 - Comments: 0

OneSpin CEO Raik Brinkmann sat down with Semiconductor Engineering to discuss how to make machine learning more robust, predictable and consistent, and new ways to identify and fix problems that may crop up as these systems are deployed. What follows are excerpts of that conversation. SE: How do we make sure devices developed with machine learning behave as they're supposed to, and how do we... » read more

EDA In The Cloud (Part 2)
By Brian Bailey - 02 May, 2018 - Comments: 0

Semiconductor Engineering sat down to discuss the migration of EDA tools into the Cloud with Arvind Vel, director of product management at ANSYS; Michal Siwinski, vice president of product management at Cadence; Richard Paw, product marketing manager at DellEMC, Gordon Allan, product manager at Mentor, a Siemens Business; Doug Letcher, president and CEO of Metrics, Tom Anderson, technical marke... » read more

The Great Chip Shakeup
By Ed Sperling - 26 Apr, 2018 - Comments: 2

Facebook, Alibaba, Google, Apple and Samsung are all designing their own chips. So are Cisco and Huawei. So what exactly does this mean for big chipmakers and the semiconductor ecosystem? While your first impulse might be to draw a straight line between Qualcomm's decision to cut 1,500 jobs and reports about giant systems companies developing chips in-house, it's not clear there is any corre... » read more

Neural Nets In ADAS And Autonomous Driving SoC Designs
By Kurt Shuler - 26 Apr, 2018 - Comments: 0

Automotive electronics has ushered in a new wave of semiconductor design innovation and one new technology gaining a lot of attention is neural networks (NNs). Advanced driving assistance systems (ADAS) and autonomous car designs now rely on NNs to meet the real-time requirements of complex object-recognition algorithms. The concept of NNs has been around since World War II, promising a futu... » read more

Deep Learning And The Future
By Mike Gianfagna - 26 Apr, 2018 - Comments: 1

Following up from my last post on our deep learning event at the Computer History Museum – “ASICs Unlock Deep Learning Innovation,” I’d like to take a glimpse into the future. Like many such discussions, it’s often useful to take a look back first to try and make sense out of what is to come.  That’s essentlially what our keynote speaker, Ty Garibay, did at the event. Ty is the CTO... » read more

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