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Power Optimization: What’s Next?


Concerns about the power consumed by semiconductors has been on the rise for the past couple of decades, but what can we expect to see coming in terms of analysis and automation from EDA companies, and is the industry ready to make the investment? Ever since Dennard scaling stopped providing automatic power gains by going to a smaller geometry, circa 2006, semiconductors have been increasing... » read more

Week In Review: Design, Low Power


Siemens Digital Industries Software acquired Fractal Technologies, a provider of tools for IP validation and comparison checks of standard cell libraries, IO, and hard IP that reports mismatches or modeling errors, as well as comparing new IP releases close to tape-out. Siemens plans to add Fractal’s technology to the Xcelerator portfolio, joining the Solido software product family, which inc... » read more

Trends In FPGA Verification Effort And Technology Adoption


The more we know about the bigger picture, context, historical and projected trends, or simply how other people do the same thing we do, the more efficiently and successfully we can do our specific jobs. This perspective also informs the EDA industry in how to best assist and sustain the needs of the FPGA and ASIC engineering communities. Providing this kind of information is the reason we c... » read more

Mapping Heat Across A System


Thermal issues are becoming more difficult to resolve as chip features get smaller and systems get faster and more complex. They now require the integration of technologies from both the design and manufacturing flows, making design for power and heat a much broader problem. This is evident with the evolution of a smart phone. Phones sold 10 years ago were very different devices. Functionali... » read more

Machine Learning At The Edge


Moving machine learning to the edge has critical requirements on power and performance. Using off-the-shelf solutions is not practical. CPUs are too slow, GPUs/TPUs are expensive and consume too much power, and even generic machine learning accelerators can be overbuilt and are not optimal for power. In this paper, learn about creating new power/memory efficient hardware architectures to meet n... » read more

Blog Review: May 12


Cadence's Claire Ying points to major changes in PCIe 6.0 as PAM4 signaling replaces NRZ to help double bandwidth, Forward Error Correction helps maintain data integrity, and various improvements are made to power consumption. Synopsys' Samantha Beaumont argues that automotive sensors are a major potential attack point and addresses some of the key areas of sensor vulnerability and the chall... » read more

Chip Monitoring And Test Collaborate


As on-chip monitoring becomes more prevalent in complex advanced-node ICs, it’s easy to question whether or not it conflicts with conventional silicon testing. It might even supplant such testing in the future. Or alternatively, they could interact, with each supporting the other. “On-chip monitors provide fine-grained observability into effects and issues that are otherwise difficult or... » read more

Testing Analog Circuits Becoming More Difficult


Foundries and packaging houses are wrestling how to control heat in the testing phase, particularly as devices continue to shrink and as thermally sensitive analog circuits are added into SoCs and advanced packages to support everything from RF to AI. The overriding problem is that heat can damage chips or devices under test. That's certainly true for digital chips developed at advanced node... » read more

Standards, Open Source, and Tools


Experts at the Table: Semiconductor Engineering discussed what open source verification means today and what it should evolve into with Jean-Marie Brunet, senior director for the Emulation Division at Siemens EDA; Ashish Darbari, CEO of Axiomise; Simon Davidmann, CEO of Imperas Software; Serge Leef, program manager in the Microsystems Technology Office at DARPA; Tao Liu, staff hardware engineer... » read more

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

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