Blog Review: April 5


Synopsys's Gordon Cooper argues that AI transformer models, initially developed for natural language processing such as translation and question answering, are starting to make inroads in the computer vision application landscape and changing the direction of deep-learning architectures. Siemens' Patrick Hope shows how to identify opportunities to optimize a PCB design through the creation o... » read more

Mechanical Challenges Rise With Heterogeneous Integration


Companies integrating multiple chips or chiplets into a package will need to address structural and other mechanical engineering issues, but gaps in the design tools, new materials and interconnect technologies, and a shortage of expertise are making it difficult to address those issues. Throughout most of the history of the semiconductors, few people outside of foundries worried about struc... » read more

Do Necessary Tools Exist For RISC-V Verification?


Semiconductor Engineering sat down to discuss the verification of RISC-V processors with Pete Hardee, group director for product management at Cadence; Mike Eftimakis, vice president for strategy and ecosystem at Codasip; Simon Davidmann, founder and CEO of Imperas Software; Sven Beyer, program manager for processor verification at Siemens EDA; Kiran Vittal, senior director of alliances partner... » read more

Options Widen For Optimizing IoT Designs


Creating a successful IoT design requires a deep understanding of the use cases, and a long list of tradeoffs among various components and technologies to provide the best solution at the right price point. Maximizing features and functions while minimizing costs is an ongoing balancing act, and the number of choices can be overwhelming. The menu includes SoC selection, OS and software proto... » read more

Blog Review: March 29


Siemens' Heather George suggests adopting a shift-left strategy for complex designs that integrate multiple dies into a package and examines the challenges and opportunities for performing comprehensive tests on 2.5D and 3D IC designs. Synopsys' Shekhar Kapoor notes that when considering whether a system will perform as intended, techniques that work well for monolithic SoCs may not be as we... » read more

Blog Review: March 22


Siemens EDA's Dan Yu warns that the unavailability of verification data is slowing down the development of advanced machine learning for verification, with valuable data assets either siloed among different team members or projects or simply discarded due to the lack of analytic techniques to extract value from them. Synopsys' Richard Solomon and Dana Neustadter point to the need for hardwar... » read more

Blog Review: March 15


Siemens EDA's Dan Yu finds that high-quality, well-connected mass data are crucial to the success of applying machine learning to verification and recommends teams pivot to a data-centric workflow. Synopsys' Shankar Krishnamoorthy suggests that deploying AI-driven chip design and verification can free teams from iterative work, letting them focus instead on product differentiation and PPA en... » read more

Week In Review: Auto, Security, Pervasive Computing


Rambus will begin selling Arm's CryptoCell embedded security platform and CryptoIsland root-of-trust cores, setting the stage for a much broader push by Rambus into security for a wide range of connected devices, and ultimately into security as a service. Under the terms of the deal, Rambus' customers will be able to license Arm IP directly from Rambus. For Arm's existing customers, there will ... » read more

Week In Review: Design, Low Power


Arm is expected to list solely on a U.S. stock exchange when it goes public again later this year, forgoing the London Stock Exchange for now, the BBC reports. Global investment banks expect the offering to value the company between $30 billion and $70 billion, according to Bloomberg. Disaggregating chips into specialized processors, memories, and architectures is becoming necessary for cont... » read more

Spark On AWS Graviton2 Best Practices: K-Means Clustering Case Study


This report focuses on how to tune a Spark application to run on a cluster of instances. We define the concepts for the cluster/Spark parameters, and explain how to configure them given a specific set of resources. We use a K-Means machine learning algorithm as a case study to analyze and tune the parameters to achieve the required performance while optimally using the available resources. W... » read more

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